TWI737105B - Disaster guarantee resource calculation method, device, computer device and storage medium - Google Patents

Disaster guarantee resource calculation method, device, computer device and storage medium Download PDF

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TWI737105B
TWI737105B TW108148468A TW108148468A TWI737105B TW I737105 B TWI737105 B TW I737105B TW 108148468 A TW108148468 A TW 108148468A TW 108148468 A TW108148468 A TW 108148468A TW I737105 B TWI737105 B TW I737105B
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disaster
status data
calculation model
data
loss
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TW202125388A (en
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王士承
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新加坡商鴻運科股份有限公司
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Abstract

The present disclosure provides a disaster guarantee resource calculation method, a disaster guarantee resource calculation device, a computer device and a computer storage medium. The method includes: obtaining disaster prevention condition data of an evaluating place, and loss status data of the evaluating place in a disaster scene, wherein the items in the disaster prevention condition data include environment state information, object status information and the number of people; inputting the disaster prevention condition data and the loss status data into a preset calculation model, and outputting disaster guarantee resources needed by the evaluating place in the disaster scene.

Description

災害保障資源計算方法、裝置、電腦裝置及存儲介質 Disaster guarantee resource calculation method, device, computer device and storage medium

本發明涉及資料處理技術領域，具體涉及一種災害保障資源計算方法、災害保障資源計算裝置、電腦裝置及電腦存儲介質。 The invention relates to the technical field of data processing, in particular to a disaster guarantee resource calculation method, a disaster guarantee resource calculation device, a computer device and a computer storage medium.

隨著災害預防之理念深入人心，人們經常會在災害來臨之前對可能造成之災難進行預防，越來越多之企業用戶，在經營過程中經常需要創建一些保障帳戶並在保障帳戶中存儲預設之資源作為保障，所述存儲之資源在災害發生之前，由企業之合作商保管，當所述企業發生災害時，所述保障帳戶中之資源會由企業之合作商按照預設比例轉移至企業之帳戶，作為企業抵抗災害之保障。因此所述保障帳戶中預存之災害保障資源之多少至關重要，現有之災害保障資源之計算方法效率低，不智慧。 As the concept of disaster prevention takes root in the hearts of the people, people often prevent possible disasters before the disaster strikes. More and more business users often need to create some protection accounts and store presets in the protection accounts during their operations. As a guarantee, the stored resources will be kept by the company’s partner before the disaster occurs. When the company has a disaster, the resources in the guarantee account will be transferred to the company by the company’s partner in a preset proportion The account is used as a guarantee for enterprises to resist disasters. Therefore, the amount of disaster protection resources pre-stored in the protection account is very important, and the existing calculation methods for disaster protection resources are inefficient and unwise.

鑒於以上內容，有必要提出一種災害保障資源計算方法、災害保障資源計算裝置、電腦裝置和電腦存儲介質，災害保障資源計算以更加高效、智慧之方式進行。 In view of the above, it is necessary to propose a disaster protection resource calculation method, a disaster protection resource calculation device, a computer device and a computer storage medium, and the disaster protection resource calculation can be carried out in a more efficient and intelligent way.

本申請之第一方面提供一種災害保障資源計算方法，所述方法包括： 獲取待評估場所之防災狀況資料，以及所述待評估場所在災害場景中之損失狀況資料，其中所述防災狀況資料中之專案包括環境狀態資訊、物品狀態資訊、人員數量；將所述防災狀況資料、損失狀況資料登錄到預設之計算模型中，輸出所述待評估場所在所述災害場景中所需之災害保障資源。 The first aspect of this application provides a method for calculating disaster protection resources, the method includes: Obtain the disaster prevention status data of the place to be evaluated, and the loss situation data of the place to be evaluated in the disaster scenario, where the items in the disaster prevention status data include environmental status information, item status information, and the number of personnel; and the disaster prevention status The data and the loss status data are registered in a preset calculation model, and the disaster protection resources required by the place to be assessed in the disaster scenario are output.

優選地，獲取所述待評估場所在災害場景中之損失狀況資料之方法包括：獲取待評估場所之防災狀況資料，藉由災害數值模擬系統對所述待評估場所進行不同災害場景下之模擬，並計算所述不同災害場景下所述待評估場所之損失狀況資料。 Preferably, the method for obtaining the loss status data of the site to be assessed in a disaster scenario includes: obtaining data on the disaster prevention status of the site to be assessed, and using a disaster numerical simulation system to simulate the site to be assessed in different disaster scenarios, And calculate the loss status data of the places to be assessed in the different disaster scenarios.

優選地，所述方法還包括：藉由災害預測模擬系統對所述待評估場所之防災狀況資料進行模擬，並判斷隨著所述防災狀況資料之變化是否會導致災害；其中，判斷是否會導致災害之方法包括：按照每種場所之防災狀況資料之變化範圍分為多個區間；按照所述區間之變化規律將所述每種場所之防災狀況資料按照不同之變化量依次輸入到災害預測模擬系統中；若所述資料之變化會觸發導致災害之條件，則確定所述資料能夠導致災害，其中導致災害之條件包括溫度、空氣中粉塵含量、空氣中有害氣體濃度。 Preferably, the method further includes: simulating the disaster prevention status data of the place to be assessed by a disaster prediction simulation system, and judging whether the change of the disaster prevention status data will cause a disaster; wherein, determining whether it will cause Disaster methods include: dividing the disaster prevention status data into multiple sections according to the variation range of the disaster prevention status data of each location; inputting the disaster prevention status data of each location to the disaster prediction simulation according to the variation law of the interval. In the system; if the change in the data triggers the conditions that cause the disaster, it is determined that the data can cause the disaster. The conditions that cause the disaster include temperature, dust content in the air, and concentration of harmful gases in the air.

優選地，所述藉由災害數值模擬系統對所述待評估場所進行不同災害場景下之模擬，並計算所述不同災害場景下所述待評估場所之損失狀況資料之方法包括：設置所述待評估場所中每一物體在每一災害中之單位時間內之損失比例；按照預設比例對所述物體進行分割，分割後之每一區域代表所述物體在災害中單位時間內損失之最小金額； 根據所述物體在每一災害中之單位時間內之損失比例、在火災中單位時間內損失之最小金額計算每一災害場景下所述場所之損失狀況資料。 Preferably, the method of performing simulations under different disaster scenarios on the place to be assessed by a disaster numerical simulation system and calculating the loss status data of the place to be assessed in the different disaster scenarios includes: setting the place to be assessed Evaluate the loss ratio of each object in each disaster per unit time in the site; divide the object according to the preset ratio, and each area after the division represents the minimum amount of loss per unit time of the object in the disaster ； Calculate the loss status data of the place in each disaster scenario according to the loss ratio of the object in each disaster per unit time and the minimum amount of loss per unit time in the fire.

優選地，所述計算模型之訓練步驟包括：獲取不同場所之防災狀況資料、所述場所在災害場景中之損失狀況資料、所述場所在所述災害場景中所需之災害保障資源，並對每一所述場所之防災狀況資料和所述場所在災害場景中之損失狀況資料、所述場所在所述災害場景中所需之災害保障資源對應存儲，並將所述場所之防災狀況資料、損失狀況資料以及災害保障資料分為訓練集和驗證集；建立基於神經網路之計算模型，並利用所述訓練集對所述計算模型之參數進行訓練，其中將所述訓練集中之防災狀況資料、損失狀況資料作為所述模型之輸入資料，所述災害保障資料作為所述模型之輸出資料；利用所述驗證集對訓練後之計算模型進行驗證，並根據驗證結果統計得到所述模型預測準確率；判斷所述模型預測準確率是否小於預設閾值；若所述模型預測準確率不小於所述預設閾值，則結束對所述計算模型之訓練。 Preferably, the training step of the calculation model includes: obtaining disaster prevention status data of different places, loss status data of the places in the disaster scene, disaster protection resources required by the place in the disaster scene, and The disaster prevention status data of each site and the loss status data of the site in the disaster scenario, the disaster protection resources required by the site in the disaster scenario are correspondingly stored, and the disaster prevention status data of the site, Loss status data and disaster protection data are divided into training set and verification set; a neural network-based calculation model is established, and the training set is used to train the parameters of the calculation model, wherein the disaster prevention status data in the training set , The loss status data is used as the input data of the model, and the disaster protection data is used as the output data of the model; the calculation model after training is verified by the verification set, and the prediction accuracy of the model is obtained according to the statistics of the verification results. Determine whether the model prediction accuracy rate is less than the preset threshold; if the model prediction accuracy rate is not less than the preset threshold, the training of the calculation model is ended.

優選地，所述方法還包括：若所述模型預測準確率小於所述預設閾值，則調整所述計算模型之結構，並利用所述訓練集重新對調整後之計算模型進行訓練，其中，所述計算模型之結構包括卷積核之數量、池化層中元素之數量、全連接層中元素之數量中之至少一種；利用所述驗證集對重新訓練之計算模型進行驗證，並根據驗證結果重新統計得到調整後之計算模型之模型預測準確率，並判斷調整後之計算模型之預測準確率是否小於所述預設閾值；若所述重新統計得到之模型預測準確率不小於所述預設閾值，則結束對所述計算模型之訓練； 若所述重新統計得到之模型預測準確率小於所述預設閾值，則重複上述調整及訓練之步驟直至藉由所述驗證集驗證得到之模型預測準確率不小於所述預設閾值。 Preferably, the method further includes: if the model prediction accuracy rate is less than the preset threshold, adjusting the structure of the calculation model, and using the training set to retrain the adjusted calculation model, wherein, The structure of the calculation model includes at least one of the number of convolution kernels, the number of elements in the pooling layer, and the number of elements in the fully connected layer; the verification set is used to verify the retrained calculation model, and according to the verification As a result, re-statistics obtain the model prediction accuracy rate of the adjusted calculation model, and determine whether the prediction accuracy rate of the adjusted calculation model is less than the preset threshold; if the model prediction accuracy rate obtained by the re-statistics is not less than the predicted Set the threshold, then the training of the calculation model is ended; If the model prediction accuracy rate obtained by the re-statistics is less than the preset threshold value, the above adjustment and training steps are repeated until the model prediction accuracy rate obtained by the verification set verification is not less than the preset threshold value.

本申請之第二方面提供一種災害保障資源計算裝置，所述裝置包括：獲取模組，用於獲取待評估場所之防災狀況資料，以及所述場所在災害場景中之損失狀況資料，其中所述防災狀況資料中之專案包括環境狀態資訊、物品狀態資訊、人員數量；計算模組，用於將所述防災狀況資料、損失狀況資料登錄到預設之計算模型中，輸出所述待評估場所在所述災害場景中所需之災害保障資料。 The second aspect of this application provides a disaster protection resource calculation device, the device includes: an acquisition module for acquiring disaster prevention status data of a place to be evaluated, and loss status data of the place in a disaster scenario, wherein the The items in the disaster prevention status data include environmental status information, item status information, and the number of personnel; the calculation module is used to register the disaster prevention status data and loss status data into the preset calculation model, and output the location of the place to be evaluated Disaster protection data required in the disaster scenario.

本申請之第三方面提供一種電腦裝置，所述電腦裝置包括處理器，所述處理器用於執行記憶體中存儲之電腦程式時實現如前所述災害保障資源計算方法。 A third aspect of the present application provides a computer device, the computer device includes a processor, and the processor is used to implement the aforementioned disaster protection resource calculation method when executing a computer program stored in a memory.

本申請之第四方面提供一種電腦存儲介質，其上存儲有電腦程式，所述電腦程式被處理器執行時實現如前所述災害保障資源計算方法。 The fourth aspect of the present application provides a computer storage medium on which a computer program is stored, and the computer program is executed by a processor to implement the aforementioned disaster protection resource calculation method.

本發明災害保障資源計算方法、災害保障資源計算裝置、電腦裝置和電腦存儲介質藉由將待評估場所之防災狀況資料以及所述場所在災害場景中之損失狀況資料登錄到預設之計算模型中，輸出所述待評估場所在所述災害場景中所需之災害保障資源，藉由所述方法可以更加準確、快速之得到所述待評估場所所需之災害保障資源。 The disaster protection resource calculation method, the disaster protection resource calculation device, the computer device and the computer storage medium of the present invention register the disaster prevention status data of the place to be evaluated and the loss status data of the place in the disaster scene into a preset calculation model To output the disaster protection resources required by the place to be assessed in the disaster scenario, and the disaster protection resources required by the place to be evaluated can be obtained more accurately and quickly by the method.

1:使用者終端 1: User terminal

2:電腦裝置 2: computer device

10:災害保障資源計算裝置 10: Disaster protection resource calculation device

20:記憶體 20: memory

30:處理器 30: processor

40:電腦程式 40: computer program

101:獲取模組 101: Get modules

102:計算模組 102: calculation module

圖1是本發明實施例一提供之災害保障資源計算方法之應用環境架構示意圖。 FIG. 1 is a schematic diagram of the application environment architecture of the disaster protection resource calculation method provided by the first embodiment of the present invention.

圖2是本發明實施例二提供之災害保障資源計算方法流程圖。 Fig. 2 is a flowchart of a method for calculating disaster protection resources provided by the second embodiment of the present invention.

圖3是本發明實施例三提供之災害保障資源計算裝置之結構示意圖。 FIG. 3 is a schematic structural diagram of a disaster protection resource calculation device provided by the third embodiment of the present invention.

圖4是本發明實施例四提供之電腦裝置示意圖。 FIG. 4 is a schematic diagram of a computer device provided by the fourth embodiment of the present invention.

為了能夠更清楚地理解本發明之上述目的、特徵和優點，下面結合附圖和具體實施例對本發明進行詳細描述。需要說明之是，在不衝突之情況下，本申請之實施例及實施例中之特徵可以相互組合。 In order to be able to understand the above objectives, features and advantages of the present invention more clearly, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other if there is no conflict.

在下面之描述中闡述了很多具體細節以便於充分理解本發明，所描述之實施例僅僅是本發明一部分實施例，而不是全部之實施例。基於本發明中之實施例，本領域普通技術人員在沒有做出創造性勞動前提下所獲得之所有其他實施例，都屬於本發明保護之範圍。 In the following description, many specific details are explained in order to fully understand the present invention. The described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

除非另有定義，本文所使用之所有之技術和科學術語與屬於本發明之技術領域之技術人員通常理解之含義相同。本文中在本發明之說明書中所使用之術語只是為了描述具體之實施例之目的，不是旨在於限制本發明。 Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments, and are not intended to limit the present invention.

參閱圖1所示，為本發明實施例一提供之災害保障資源計算方法之應用環境架構示意圖。 Refer to FIG. 1, which is a schematic diagram of the application environment architecture of the disaster protection resource calculation method provided by the first embodiment of the present invention.

本發明中之災害保障資源計算方法應用在使用者終端1中，所述使用者終端1和一個電腦裝置2藉由網路建立通信連接。所述網路可以是有線網路，也可以是無線網路，例如無線電、無線保真(Wireless Fidelity,WIFI)、蜂窩、衛星、廣播等。所述使用者終端1用於獲取待評估場所之防災狀況資料，以及所述場所在災害場景中之損失狀況資料，利用預設之計算模型分析所述待評估場所在所述災害場景中所需之災害保障資源，所述電腦裝置2用於存儲不同場所之防災狀況資料、所述場所在災害場景中之損失狀況資料、所述場所在所述災害 場景中所需之災害保障資源。 The disaster protection resource calculation method of the present invention is applied to the user terminal 1, and the user terminal 1 and a computer device 2 establish a communication connection through the network. The network can be a wired network or a wireless network, such as radio, wireless fidelity (WIFI), cellular, satellite, broadcast, etc. The user terminal 1 is used to obtain the disaster prevention status data of the place to be evaluated, and the loss status data of the place in the disaster scene, and use a preset calculation model to analyze the need for the place to be evaluated in the disaster scene. The computer device 2 is used to store the disaster prevention status data of different places, the loss status data of the place in the disaster scene, and the place in the disaster The disaster protection resources needed in the scene.

所述使用者終端1可以為安裝有災害保障資源計算方法軟體之電子設備，例如個人電腦、平板電腦等。 The user terminal 1 may be an electronic device installed with disaster protection resource calculation method software, such as a personal computer, a tablet computer, and the like.

所述電腦裝置2是可以為存儲有不同場所之防災狀況資料、所述場所在災害場景中之損失狀況資料、所述場所在所述災害場景中所需之災害保障資源之電子設備，例如個人電腦、伺服器等，其中，所述伺服器可以是單一之伺服器、伺服器集群或雲伺服器等。 The computer device 2 may be an electronic device that stores disaster prevention status data of different places, loss status data of the place in the disaster scene, and disaster protection resources required by the place in the disaster scene, such as personal Computers, servers, etc., where the server can be a single server, a server cluster, or a cloud server.

在本發明又一實施方式中，所述不同場所之防災狀況資料、所述場所在災害場景中之損失狀況資料、所述場所在所述災害場景中所需之災害保障資源也可以存儲於使用者終端1中。 In another embodiment of the present invention, the disaster prevention status data of the different places, the loss status data of the places in the disaster scene, and the disaster protection resources required by the place in the disaster scene can also be stored in the use者Terminal 1.

實施例二 Example two

請參閱圖2所示，是本發明第二實施例提供之災害保障資源計算方法之流程圖。根據不同之需求，所述流程圖中步驟之順序可以改變，某些步驟可以省略。 Please refer to FIG. 2, which is a flowchart of the disaster protection resource calculation method provided by the second embodiment of the present invention. According to different needs, the order of the steps in the flowchart can be changed, and some steps can be omitted.

步驟S1、獲取待評估場所之防災狀況資料，以及所述場所在災害場景中之損失狀況資料。 Step S1: Obtain the disaster prevention status data of the place to be assessed, and the loss status data of the place in the disaster scene.

所述防災狀況資料中之專案包括環境狀態資訊、物品狀態資訊、人員數量。其中所述環境狀態資訊可以包括固定式消防設施資訊、移動式消防設施資訊、排風設施資訊、環境溫濕度資訊，例如：消防警報器、煙霧報警器、天花板消防噴淋頭、火災探測器、消火栓之數量和擺放位置、排風口之數量及位置等。所述物品狀態訊息包括待評估場所內之物品名稱、物品擺放位置資訊，例如製造設備、材料、辦公電腦、傢俱之數量和擺放位置等。 The items in the disaster prevention status data include environmental status information, item status information, and the number of personnel. The environmental status information may include information on fixed fire-fighting facilities, information on mobile fire-fighting facilities, information on ventilation facilities, and information on ambient temperature and humidity, such as fire alarms, smoke alarms, ceiling fire sprinklers, fire detectors, The number and location of fire hydrants, the number and location of exhaust outlets, etc. The item status information includes item name and item placement information in the place to be evaluated, such as manufacturing equipment, materials, office computers, the quantity and placement of furniture, etc.

一個實施方式中，所述待評估場所之防災狀況資料獲取方式可以包括：接收用戶輸入之所述待評估場所之疏散人員數量、消防設施之種類和數 量、排風口之數量和位置、物品之種類、數量、擺放位置等資訊。 In one embodiment, the method for obtaining disaster prevention status data of the place to be assessed may include: receiving user input for the number of evacuated persons in the place to be assessed, and the type and number of fire-fighting facilities. Information such as the amount, the number and location of exhaust outlets, the type, quantity, and placement of items.

另一個實施方式中，所述防災狀況資料獲取方法還可以是藉由接收多個攝像裝置採集之待評估場所之多張圖像，藉由圖像識別方法識別所述圖像中疏散人員數量、消防設施之種類和數量、排風口之數量和位置、物品之種類、數量、擺放位置等資訊。 In another embodiment, the method for acquiring disaster prevention status data may also be by receiving multiple images of the place to be evaluated collected by multiple camera devices, and identifying the number of evacuated persons in the images by the image recognition method, Information about the type and quantity of fire-fighting facilities, the quantity and location of exhaust outlets, the type, quantity, and placement of items.

在一實施方式中，所述獲取所述場所在災害場景中之損失狀況資料之方法包括：獲取待評估場所之防災狀況資料，藉由災害數值模擬系統對所述場所進行不同災害場景下之模擬，並計算所述不同災害場景下所述場所之損失狀況資料。所述不同災害場景下所述場所之損失狀況資料之計算方法包括設置所述場所中每一物體在單位時間內之損失比例，並按照預設比例對所述物體分割，分割後之每一區域代表所述可燃物在火災中單位時間內損失之最小金額；根據所述物體在單位時間內之損失比例、在災害場景中單位時間內損失之最小金額計算不同災害場景中所述場所之損失狀況資料。 In one embodiment, the method for obtaining the loss status data of the site in the disaster scenario includes: obtaining the disaster prevention status data of the site to be assessed, and using a disaster numerical simulation system to simulate the site in different disaster scenarios , And calculate the loss status data of the places in the different disaster scenarios. The calculation method of the loss status data of the place in the different disaster scenarios includes setting the loss ratio of each object in the place per unit time, and dividing the object according to a preset ratio, and each area after the division Represents the minimum amount of loss of the combustibles per unit time in the fire; calculates the loss status of the places in different disaster scenarios based on the proportion of the object’s loss per unit time and the minimum amount of loss per unit time in the disaster scenario material.

例如將一台光刻機進行81等分，所述光刻機之價值是81萬，每一等分之價值為1萬，每一等分之燃燒時間是2分鐘。根據光刻機所處之災害場景，查詢所述光刻機在不同災害中之單位時間內損失比例。例如所述光刻機在具有消防噴淋裝置、自動消防報警裝置之火災場景中之一分鐘之損失比例為2%，可以計算出所述光刻機在30分鐘之火災場所中之損失資料為15萬。 For example, a lithography machine is divided into 81 equal parts, the value of the lithography machine is 810,000, the value of each equal part is 10,000, and the burning time of each equal part is 2 minutes. According to the disaster scene where the lithography machine is located, query the ratio of loss per unit time of the lithography machine in different disasters. For example, the loss ratio of one minute of the lithography machine in a fire scene with a fire sprinkler device and an automatic fire alarm device is 2%, and the loss data of the lithography machine in a 30-minute fire place can be calculated as 150000.

在本發明又一實施方式中，所述獲取所述場所在災害場景中之損失狀況資料之方法還包括：獲取待評估場所之防災狀況資料，藉由災害預測模擬系統對所述場所之防災狀況資料進行模擬，並判斷隨著所述防災狀況資料之變化是否會導致災害；其中，判斷之方法包括：按照每種場所之防災狀況資料之變化範圍分為多 個預設區間；按照所述區間之變化規律將所述每種場所之防災狀況資料按照不同之變化量依次輸入到災害預測模擬系統中；若所述資料之變化會觸發導致災害之條件，則確定所述資料能夠導致災害，其中導致災害之條件包括溫度、空氣中粉塵含量、空氣中有害氣體濃度。 In still another embodiment of the present invention, the method for obtaining data on the loss status of the site in a disaster scenario further includes: obtaining data on the disaster prevention status of the site to be assessed, and using a disaster prediction simulation system to monitor the disaster prevention status of the site The data is simulated, and it is judged whether the change of the disaster prevention status data will cause a disaster; among them, the method of judgment includes: according to the change range of the disaster prevention status data of each place, it is divided into multiple A preset interval; according to the change rule of the interval, the disaster prevention status data of each kind of place is sequentially input into the disaster prediction simulation system according to different changes; if the change of the data will trigger the conditions that cause the disaster, then It is determined that the data can cause disasters, and the conditions that cause disasters include temperature, dust content in the air, and concentration of harmful gases in the air.

例如分析預設場所之溫度之變化是否會導致場所內空氣品質變差，其中空氣品質變差之原因是空氣中硫化物濃度之提高。在一實施方式中，所述預設場所為化工產品加工車間。將所述化工產品加工車間中之所有物品及車間之排風消防圖輸入到災害預測模擬系統中，加工車間之溫度每變化2度，藉由所述模擬系統計算出所述加工車間空氣中硫化物之濃度。並判斷隨著溫度之變化，空氣中硫化物之濃度是否發生變化，若發生變化，則預設場所之溫度是影響所述預設場所空氣變差之原因。若未發生變化，則預設場所之溫度不是影響所述預設場所空氣變差之原因。當溫度之變化能夠影響預設場所之空氣品質時，將不同溫度對應之硫化物濃度對應存儲，並記錄當硫化物濃度達到影響空氣品質之濃度時之溫度值。 For example, analyze whether changes in the temperature of a preset place will cause the air quality in the place to deteriorate. The reason for the deterioration of the air quality is the increase in the concentration of sulfide in the air. In one embodiment, the preset location is a chemical product processing workshop. Input all the items in the chemical product processing workshop and the workshop's exhaust fire fighting diagram into the disaster prediction simulation system. For every 2 degree change in the temperature of the processing workshop, the simulation system calculates the vulcanization in the air in the processing workshop The concentration of the material. And judge whether the concentration of sulfide in the air changes with the change of temperature. If it changes, the temperature of the preset place is the reason that affects the air deterioration of the preset place. If there is no change, the temperature of the preset location is not the cause that affects the air deterioration of the preset location. When the temperature change can affect the air quality in the preset place, the sulfide concentration corresponding to different temperatures is correspondingly stored, and the temperature value when the sulfide concentration reaches the concentration that affects the air quality is recorded.

步驟S2、將所述防災狀況資料、損失狀況資料登錄到預設之計算模型中，輸出所述待評估場所在所述災害場景中所需之災害保障資源。 Step S2: Log the disaster prevention status data and loss status data into a preset calculation model, and output disaster protection resources required by the place to be assessed in the disaster scenario.

舉例而言，在本發明一實施方式中，所述災害保障資源計算方法應用於保險公司之保費計算系統中。所述防災狀況資料包括待保險場所中之環境狀況資訊、物品狀況資訊、人員狀況資訊。所述損失狀況資料可以是所述待保險場所在火災中之損失狀況，所述災害保障資源是待保險場所需要投保之金額。將待保險場所之防災狀況資料、損失狀況資料登錄到預設之計算模型中，可以輸出所述待保險場所在所述災害場景中所需之保費金額。 For example, in an embodiment of the present invention, the disaster protection resource calculation method is applied to a premium calculation system of an insurance company. The disaster prevention status data includes environmental status information, item status information, and personnel status information in the place to be insured. The loss status data may be the loss status of the place to be insured during a fire, and the disaster protection resource is the amount of money to be insured for the place to be insured. The disaster prevention status data and loss status data of the place to be insured are registered in the preset calculation model, and the premium amount required by the place to be insured in the disaster scenario can be output.

所述預設之計算模型之訓練方法包括以下步驟： The training method of the preset calculation model includes the following steps:

(1)獲取不同場所之防災狀況資料、所述場所在災害場景中之損失狀況資料、所述場所在所述災害場景中所需之災害保障資源，並對每一所述場所之防災狀況資料和所述場所在災害場景中之損失狀況資料、所述場所在所述災害場景中所需之災害保障資源對應存儲，並將所述場所之防災狀況資料、損失狀況資料以及災害保障資料分為訓練集和驗證集。 (1) Obtain the disaster prevention status data of different places, the loss situation data of the said place in the disaster scene, the disaster protection resources required by the said place in the disaster scene, and the disaster prevention status data of each said place It is stored corresponding to the loss status data of the site in the disaster scene and the disaster protection resources required by the site in the disaster scenario, and the disaster prevention status data, loss status data, and disaster protection data of the site are divided into Training set and validation set.

所述場所在所述災害場景中所需之災害保障資源為所述場所歷史投入之災害保障資源，所述歷史投入之災害保障資源存儲於電腦裝置2中。 The disaster protection resources required by the site in the disaster scene are disaster protection resources historically invested by the site, and the historically invested disaster protection resources are stored in the computer device 2.

例如所述電腦裝置2是保險公司用於存儲使用者資料之電子設備，所述電子設備中存儲了歷史投保場所之保單資訊，所述保單資訊包括投保場所之防災狀況資料、根據災害數值模擬系統模擬之所述場所在災害中之損失狀況資料、以及所述投保場所之投保金額。並將所述歷史保單資訊中之資料分為訓練集和驗證集。 For example, the computer device 2 is an electronic device used by an insurance company to store user data. The electronic device stores policy information of historical insurance locations. The policy information includes disaster prevention status data of the insurance location, and a disaster numerical simulation system. The simulated loss situation data of the said place in the disaster and the insured amount of the said place insured. And divide the data in the historical policy information into a training set and a verification set.

(2)建立基於神經網路之計算模型，並利用所述訓練集對所述計算模型之參數進行訓練，其中將所述訓練集中之防災狀況資料、損失狀況資料作為所述模型之輸入資料，所述災害保障資料作為所述模型之輸出資料。 (2) Establish a calculation model based on a neural network, and use the training set to train the parameters of the calculation model, wherein the disaster prevention status data and loss status data in the training set are used as the input data of the model, The disaster protection data is used as the output data of the model.

所述基於神經網路之計算模型包括多種演算法結構，可以包括基於卷積神經網路之計算模型、基於遺傳演算法之神經網路、基於模糊理論之神經網路等。 The neural network-based calculation model includes a variety of algorithm structures, which may include a calculation model based on a convolutional neural network, a neural network based on a genetic algorithm, a neural network based on a fuzzy theory, and the like.

(3)利用所述驗證集對訓練後之計算模型進行驗證，並根據驗證結果統計得到所述模型預測準確率。 (3) Use the verification set to verify the training calculation model, and obtain statistics of the model prediction accuracy rate according to the verification result.

將驗證集中之防災狀況資料、所述場所在災害場景中之損失狀況資料登錄到所述計算模型中，計算出所述場所在所述災害場景中所需之災害保障資源，並將計算出之災害保障資源與訓練集中之災害保障資源進行比較，根據比較結果驗證所述模型之預測準確率。 The disaster prevention status data in the verification center and the loss status data of the site in the disaster scenario are registered in the calculation model, and the disaster protection resources required by the site in the disaster scenario are calculated and calculated The disaster protection resources are compared with the disaster protection resources in the training set, and the prediction accuracy of the model is verified according to the comparison results.

(4)判斷所述模型預測準確率是否小於預設閾值。 (4) Determine whether the prediction accuracy rate of the model is less than a preset threshold.

在一實施方式中，所述預測準確率為95%。 In one embodiment, the prediction accuracy rate is 95%.

(5)若所述模型預測準確率不小於所述預設閾值，則結束對所述計算模型之訓練。 (5) If the prediction accuracy rate of the model is not less than the preset threshold, the training of the calculation model is ended.

(6)若所述模型預測準確率小於所述預設閾值，則調整所述計算模型之結構，並利用所述訓練集重新對調整後之計算模型進行訓練，其中，所述計算模型之結構包括卷積核之數量、池化層中元素之數量、全連接層中元素之數量中之至少一種。 (6) If the prediction accuracy of the model is less than the preset threshold, adjust the structure of the calculation model, and use the training set to retrain the adjusted calculation model, wherein the structure of the calculation model It includes at least one of the number of convolution kernels, the number of elements in the pooling layer, and the number of elements in the fully connected layer.

(7)利用所述驗證集對重新訓練之計算模型進行驗證，並根據驗證結果重新統計得到調整後之計算模型之模型預測準確率，並判斷調整後之計算模型之預測準確率是否小於所述預設閾值。 (7) Use the verification set to verify the retrained calculation model, and re-statistically obtain the model prediction accuracy rate of the adjusted calculation model based on the verification result, and determine whether the prediction accuracy rate of the adjusted calculation model is less than the said Preset threshold.

(8)若所述重新統計得到之模型預測準確率不小於所述預設閾值，則結束對所述計算模型之訓練。 (8) If the model prediction accuracy rate obtained by the re-statistics is not less than the preset threshold, the training of the calculation model is ended.

(9)若所述重新統計得到之模型預測準確率小於所述預設閾值，則重複上述調整及訓練之步驟直至藉由所述驗證集驗證得到之模型預測準確率不小於所述預設閾值。 (9) If the model prediction accuracy rate obtained by the re-statistics is less than the preset threshold value, repeat the above adjustment and training steps until the model prediction accuracy rate obtained by the verification set verification is not less than the preset threshold value .

以上預設之計算模型之訓練方法中之步驟根據實際需要步驟之順序可以改變，某些步驟可以省略。所述訓練方法可以線上完成，也可以離線完成。 The steps in the training method of the above preset calculation model can be changed according to actual needs, and some steps can be omitted. The training method can be completed online or offline.

上述圖2詳細介紹了本發明之災害保障資源計算方法，下面結合第3-4圖，對實現所述災害保障資源計算方法之軟體裝置之功能模組以及實現所述災害保障資源計算方法之硬體裝置架構進行介紹。 The above figure 2 describes the disaster protection resource calculation method of the present invention in detail. In conjunction with Figures 3-4, the functional modules of the software device for realizing the disaster protection resource calculation method and the hardware of the disaster protection resource calculation method are described below. Introduction to the body device architecture.

應所述瞭解，所述實施例僅為說明之用，在專利申請範圍上並不受此結構之限制。 It should be understood that the embodiments are only for illustrative purposes, and are not limited by this structure in the scope of the patent application.

實施例三 Example three

圖3為本發明災害保障資源計算裝置較佳實施例之結構圖。 Fig. 3 is a structural diagram of a preferred embodiment of the disaster protection resource calculation device of the present invention.

在一些實施例中，災害保障資源計算裝置10運行於電腦裝置中。所述電腦裝置藉由網路連接了多個使用者終端。所述災害保障資源計算裝置10可以包括多個由程式碼段所組成之功能模組。所述災害保障資源計算裝置10中之各個程式段之程式碼可以存儲於電腦裝置之記憶體中，並由所述至少一個處理器所執行，以實現災害保障資源計算功能。 In some embodiments, the disaster protection resource computing device 10 runs in a computer device. The computer device is connected to a plurality of user terminals through the network. The disaster protection resource computing device 10 may include multiple functional modules composed of code segments. The code of each program segment in the disaster protection resource computing device 10 can be stored in the memory of the computer device and executed by the at least one processor to realize the disaster protection resource computing function.

本實施例中，所述災害保障資源計算裝置10根據其所執行之功能，可以被劃分為多個功能模組。參閱圖3所示，所述功能模組可以包括：獲取模組101、計算模組102。本發明所稱之模組是指一種能夠被至少一個處理器所執行並且能夠完成固定功能之一系列電腦程式段，其存儲在記憶體中。在本實施例中，關於各模組之功能將在後續之實施例中詳述。 In this embodiment, the disaster protection resource calculation device 10 can be divided into multiple functional modules according to the functions it performs. Referring to FIG. 3, the functional modules may include: an acquisition module 101 and a calculation module 102. The module referred to in the present invention refers to a series of computer program segments that can be executed by at least one processor and can complete fixed functions, which are stored in the memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

所述獲取模組101，用於獲取待評估場所之防災狀況資料，以及所述場所在災害場景中之損失狀況資料。 The acquisition module 101 is used to acquire the disaster prevention status data of the place to be evaluated and the loss status data of the place in the disaster scene.

所述防災狀況資料中之專案包括環境狀態資訊、物品狀態資訊、人員數量。其中所述環境狀態資訊可以包括固定式消防設施資訊、移動式消防設施資訊、排風設施資訊、環境溫濕度資訊，例如：消防警報器、煙霧報警器、天花板消防噴淋頭、火災探測器、消火栓之數量和擺放位置、排風口之數量及位置等。所述物品狀態訊息包括待評估場所內之物品名稱、物品擺放位置資訊，例如製造設備、材料、辦公電腦、傢俱之數量和擺放位置等。 The items in the disaster prevention status data include environmental status information, item status information, and the number of personnel. The environmental status information may include information on fixed fire-fighting facilities, information on mobile fire-fighting facilities, information on ventilation facilities, and information on ambient temperature and humidity, such as fire alarms, smoke alarms, ceiling fire sprinklers, fire detectors, The number and location of fire hydrants, the number and location of exhaust outlets, etc. The item status information includes item name and item placement information in the place to be evaluated, such as manufacturing equipment, materials, office computers, the quantity and placement of furniture, etc.

一個實施方式中，所述待評估場所之防災狀況資料獲取方式可以包括：接收用戶輸入之所述待評估場所之疏散人員數量、消防設施之種類和數量、排風口之數量和位置、物品之種類、數量、擺放位置等資訊。 In one embodiment, the method of obtaining disaster prevention status data of the place to be assessed may include: receiving user input of the number of evacuated persons in the place to be assessed, the type and quantity of fire-fighting facilities, the number and location of air outlets, and the type of items , Quantity, placement and other information.

另一個實施方式中，所述防災狀況資料獲取方法還可以是藉由接 收多個攝像裝置採集之待評估場所之多張圖像，藉由圖像識別方法識別所述圖像中疏散人員數量、消防設施之種類和數量、排風口之數量和位置、物品之種類、數量、擺放位置等資訊。 In another embodiment, the method for obtaining disaster prevention information can also be obtained by Collect multiple images of the place to be assessed collected by multiple camera devices, and identify the number of evacuated persons in the image, the type and number of fire-fighting facilities, the number and location of air outlets, and the types of objects in the images by image recognition methods. Information such as quantity and placement.

在一實施方式中，所述獲取所述場所在災害場景中之損失狀況資料之方法包括：獲取待評估場所之防災狀況資料，藉由災害數值模擬系統對所述場所進行不同災害場景下之模擬，並計算所述不同災害場景下所述場所之損失狀況資料。所述不同災害場景下所述場所之損失狀況資料之計算方法包括設置所述場所中每一物體在單位時間內之損失比例，並按照預設比例對所述物體分割，分割後之每一區域代表所述可燃物在火災中單位時間內損失之最小金額；根據所述物體在單位時間內之損失比例、在災害場景中單位時間內損失之最小金額計算不同災害場景中所述場所之損失狀況資料。 In one embodiment, the method for obtaining the loss status data of the site in the disaster scenario includes: obtaining the disaster prevention status data of the site to be assessed, and using a disaster numerical simulation system to simulate the site in different disaster scenarios , And calculate the loss status data of the places in the different disaster scenarios. The calculation method of the loss status data of the place in the different disaster scenarios includes setting the loss ratio of each object in the place per unit time, and dividing the object according to a preset ratio, and each area after the division Represents the minimum amount of loss of the combustibles per unit time in the fire; calculates the loss status of the places in different disaster scenarios based on the proportion of the object’s loss per unit time and the minimum amount of loss per unit time in the disaster scenario material.

例如將一台光刻機進行81等分，所述光刻機之價值是81萬，每一等分之價值為1萬，每一等分之燃燒時間是2分鐘。根據光刻機所處之災害場景，查詢所述光刻機在不同災害中之單位時間內損失比例。例如所述光刻機在具有消防噴淋裝置、自動消防報警裝置之火災場景中之一分鐘之損失比例為2%，可以計算出所述光刻機在30分鐘之火災場所中之損失資料為15萬。 For example, a lithography machine is divided into 81 equal parts, the value of the lithography machine is 810,000, the value of each equal part is 10,000, and the burning time of each equal part is 2 minutes. According to the disaster scene where the lithography machine is located, query the ratio of loss per unit time of the lithography machine in different disasters. For example, the loss ratio of one minute of the lithography machine in a fire scene with a fire sprinkler device and an automatic fire alarm device is 2%, and the loss data of the lithography machine in a 30-minute fire place can be calculated as 150000.

在本發明又一實施方式中，所述獲取所述場所在災害場景中之損失狀況資料之方法還包括：獲取待評估場所之防災狀況資料，藉由災害預測模擬系統對所述場所之防災狀況資料進行模擬，並判斷隨著所述防災狀況資料之變化是否會導致災害；其中，判斷之方法包括：按照每種場所之防災狀況資料之變化範圍分為多個預設區間；按照所述區間之變化規律將所述每種場所之防災狀況資料按照不同之變化 量依次輸入到災害預測模擬系統中；若所述資料之變化會觸發導致災害之條件，則確定所述資料能夠導致災害，其中導致災害之條件包括溫度、空氣中粉塵含量、空氣中有害氣體濃度。 In still another embodiment of the present invention, the method for obtaining data on the loss status of the site in a disaster scenario further includes: obtaining data on the disaster prevention status of the site to be assessed, and using a disaster prediction simulation system to monitor the disaster prevention status of the site The data is simulated, and it is judged whether the change of the disaster prevention status data will lead to a disaster; wherein the method of judgment includes: dividing into multiple preset intervals according to the variation range of the disaster prevention status data of each place; according to the interval The law of change changes the disaster prevention status data of each type of place according to different changes The quantity is input into the disaster prediction simulation system in turn; if the change in the data triggers the conditions that cause the disaster, it is determined that the data can cause the disaster. The conditions that cause the disaster include temperature, dust content in the air, and concentration of harmful gases in the air .

例如分析預設場所之溫度之變化是否會導致場所內空氣品質變差，其中空氣品質變差之原因是空氣中硫化物濃度之提高。在一實施方式中，所述預設場所為化工產品加工車間。將所述化工產品加工車間中之所有物品及車間之排風消防圖輸入到災害預測模擬系統中，加工車間之溫度每變化2度，藉由所述模擬系統計算出所述加工車間空氣中硫化物之濃度。並判斷隨著溫度之變化，空氣中硫化物之濃度是否發生變化，若發生變化，則預設場所之溫度是影響所述預設場所空氣變差之原因。若未發生變化，則預設場所之溫度不是影響所述預設場所空氣變差之原因。當溫度之變化能夠影響預設場所之空氣品質時，將不同溫度對應之硫化物濃度對應存儲，並記錄當硫化物濃度達到影響空氣品質之濃度時之溫度值。 For example, analyze whether changes in the temperature of a preset place will cause the air quality in the place to deteriorate. The reason for the deterioration of the air quality is the increase in the concentration of sulfide in the air. In one embodiment, the preset location is a chemical product processing workshop. Input all the items in the chemical product processing workshop and the workshop's exhaust fire fighting diagram into the disaster prediction simulation system. For every 2 degree change in the temperature of the processing workshop, the simulation system calculates the vulcanization in the air in the processing workshop The concentration of the material. And judge whether the concentration of sulfide in the air changes with the change of temperature. If it changes, the temperature of the preset place is the reason that affects the air deterioration of the preset place. If there is no change, the temperature of the preset location is not the cause that affects the air deterioration of the preset location. When the temperature change can affect the air quality in the preset place, the sulfide concentration corresponding to different temperatures is correspondingly stored, and the temperature value when the sulfide concentration reaches the concentration that affects the air quality is recorded.

所述計算模組102，用於將所述防災狀況資料、損失狀況資料登錄到預設之計算模型中，輸出所述待評估場所在所述災害場景中所需之災害保障資料。 The calculation module 102 is used to register the disaster prevention status data and the loss status data into a preset calculation model, and output disaster protection data required by the place to be assessed in the disaster scenario.

舉例而言，在本發明一實施方式中，所述災害保障資源計算方法應用於保險公司之保費計算系統中。所述防災狀況資料包括待保險場所中之環境狀況資訊、物品狀況資訊、人員狀況資訊。所述損失狀況資料可以是所述待保險場所在火災中之損失狀況，所述災害保障資源是待保險場所需要投保之金額。將待保險場所之防災狀況資料、損失狀況資料登錄到預設之計算模型中，可以輸出所述待保險場所在所述災害場景中所需之保費金額。 For example, in an embodiment of the present invention, the disaster protection resource calculation method is applied to a premium calculation system of an insurance company. The disaster prevention status data includes environmental status information, item status information, and personnel status information in the place to be insured. The loss status data may be the loss status of the place to be insured during a fire, and the disaster protection resource is the amount of money to be insured for the place to be insured. The disaster prevention status data and loss status data of the place to be insured are registered in the preset calculation model, and the premium amount required by the place to be insured in the disaster scenario can be output.

所述預設之計算模型之訓練方法包括以下步驟： The training method of the preset calculation model includes the following steps:

(1)獲取不同場所之防災狀況資料、所述場所在災害場景中之損 失狀況資料、所述場所在所述災害場景中所需之災害保障資源，並對每一所述場所之防災狀況資料和所述場所在災害場景中之損失狀況資料、所述場所在所述災害場景中所需之災害保障資源對應存儲，並將所述場所之防災狀況資料、損失狀況資料以及災害保障資料分為訓練集和驗證集。 (1) Obtain information on the disaster prevention status of different places and the damage of said places in the disaster scene Loss situation data, disaster protection resources required by the site in the disaster scenario, and data on the disaster prevention situation of each site and the loss situation data of the site in the disaster scenario, the site in the disaster scenario The disaster protection resources required in the disaster scene are correspondingly stored, and the disaster prevention status data, loss status data, and disaster protection data of the site are divided into a training set and a verification set.

所述場所在所述災害場景中所需之災害保障資源為所述場所歷史投入之災害保障資源，所述歷史投入之災害保障資源存儲於電腦裝置2中。 The disaster protection resources required by the site in the disaster scene are disaster protection resources historically invested by the site, and the historically invested disaster protection resources are stored in the computer device 2.

例如所述電腦裝置2是保險公司用於存儲使用者資料之電子設備，所述電子設備中存儲了歷史投保場所之保單資訊，所述保單資訊包括投保場所之防災狀況資料、根據災害數值模擬系統模擬之所述場所在災害中之損失狀況資料、以及所述投保場所之投保金額。並將所述歷史保單資訊中之資料分為訓練集和驗證集。 For example, the computer device 2 is an electronic device used by an insurance company to store user data. The electronic device stores policy information of historical insurance locations. The policy information includes disaster prevention status data of the insurance location, and a disaster numerical simulation system. The simulated loss situation data of the said place in the disaster and the insured amount of the said place insured. And divide the data in the historical policy information into a training set and a verification set.

(2)建立基於神經網路之計算模型，並利用所述訓練集對所述計算模型之參數進行訓練，其中將所述訓練集中之防災狀況資料、損失狀況資料作為所述模型之輸入資料，所述災害保障資料作為所述模型之輸出資料。 (2) Establish a calculation model based on a neural network, and use the training set to train the parameters of the calculation model, wherein the disaster prevention status data and loss status data in the training set are used as the input data of the model, The disaster protection data is used as the output data of the model.

所述基於神經網路之計算模型包括多種演算法結構，可以包括基於卷積神經網路之計算模型、基於遺傳演算法之神經網路、基於模糊理論之神經網路等。 The neural network-based calculation model includes a variety of algorithm structures, which may include a calculation model based on a convolutional neural network, a neural network based on a genetic algorithm, a neural network based on a fuzzy theory, and the like.

(3)利用所述驗證集對訓練後之計算模型進行驗證，並根據驗證結果統計得到所述模型預測準確率。 (3) Use the verification set to verify the training calculation model, and obtain statistics of the model prediction accuracy rate according to the verification result.

將驗證集中之防災狀況資料、所述場所在災害場景中之損失狀況資料登錄到所述計算模型中，計算出所述場所在所述災害場景中所需之災害保障資源，並將計算出之災害保障資源與訓練集中之災害保障資源進行比較，根據比較結果驗證所述模型之預測準確率。 The disaster prevention status data in the verification center and the loss status data of the site in the disaster scenario are registered in the calculation model, and the disaster protection resources required by the site in the disaster scenario are calculated and calculated The disaster protection resources are compared with the disaster protection resources in the training set, and the prediction accuracy of the model is verified according to the comparison results.

(4)判斷所述模型預測準確率是否小於預設閾值。 (4) Determine whether the prediction accuracy rate of the model is less than a preset threshold.

在一實施方式中，所述預測準確率為95%。 In one embodiment, the prediction accuracy rate is 95%.

(5)若所述模型預測準確率不小於所述預設閾值，則結束對所述計算模型之訓練。 (5) If the prediction accuracy rate of the model is not less than the preset threshold, the training of the calculation model is ended.

(6)若所述模型預測準確率小於所述預設閾值，則調整所述計算模型之結構，並利用所述訓練集重新對調整後之計算模型進行訓練，其中，所述計算模型之結構包括卷積核之數量、池化層中元素之數量、全連接層中元素之數量中之至少一種。 (6) If the prediction accuracy of the model is less than the preset threshold, adjust the structure of the calculation model, and use the training set to retrain the adjusted calculation model, wherein the structure of the calculation model It includes at least one of the number of convolution kernels, the number of elements in the pooling layer, and the number of elements in the fully connected layer.

(7)利用所述驗證集對重新訓練之計算模型進行驗證，並根據驗證結果重新統計得到調整後之計算模型之模型預測準確率，並判斷調整後之計算模型之預測準確率是否小於所述預設閾值。 (7) Use the verification set to verify the retrained calculation model, and re-statistically obtain the model prediction accuracy rate of the adjusted calculation model based on the verification result, and determine whether the prediction accuracy rate of the adjusted calculation model is less than the said Preset threshold.

(8)若所述重新統計得到之模型預測準確率不小於所述預設閾值，則結束對所述計算模型之訓練。 (8) If the model prediction accuracy rate obtained by the re-statistics is not less than the preset threshold, the training of the calculation model is ended.

(9)若所述重新統計得到之模型預測準確率小於所述預設閾值，則重複上述調整及訓練之步驟直至藉由所述驗證集驗證得到之模型預測準確率不小於所述預設閾值。 (9) If the model prediction accuracy rate obtained by the re-statistics is less than the preset threshold value, repeat the above adjustment and training steps until the model prediction accuracy rate obtained by the verification set verification is not less than the preset threshold value .

以上預設之計算模型之訓練方式中之步驟根據實際需要步驟之順序可以改變，某些步驟可以省略。所述訓練方法可以線上完成，也可以離線完成。 The steps in the training method of the above preset calculation model can be changed according to actual needs, and some steps can be omitted. The training method can be completed online or offline.

實施例四 Example four

圖4為本發明電腦裝置較佳實施例之示意圖。 Fig. 4 is a schematic diagram of a preferred embodiment of the computer device of the present invention.

所述電腦裝置1包括記憶體20、處理器30以及存儲在所述記憶體20中並可在所述處理器30上運行之電腦程式40，例如災害保障資源計算程式。所述處理器30執行所述電腦程式40時實現上述災害保障資源計算方法實施例中之步驟，例如圖2所示之步驟S1~S2。或者，所述處理器30執行所述電腦程式40時 實現上述災害保障資源計算裝置實施例中各模組/單元之功能，例如圖3中之單元101-102。 The computer device 1 includes a memory 20, a processor 30, and a computer program 40 stored in the memory 20 and running on the processor 30, such as a disaster protection resource calculation program. When the processor 30 executes the computer program 40, the steps in the embodiment of the method for calculating disaster protection resources are implemented, such as steps S1 to S2 shown in FIG. 2. Or, when the processor 30 executes the computer program 40 The functions of the modules/units in the embodiment of the above disaster protection resource calculation device are realized, such as the units 101-102 in FIG. 3.

示例性之，所述電腦程式40可以被分割成一個或多個模組/單元，所述一個或者多個模組/單元被存儲在所述記憶體20中，並由所述處理器30執行，以完成本發明。所述一個或多個模組/單元可以是能夠完成特定功能之一系列電腦程式指令段，所述指令段用於描述所述電腦程式40在所述電腦裝置1中之執行過程。例如，所述電腦程式40可以被分割成圖3中之獲取模組101、計算模組102。 Exemplarily, the computer program 40 may be divided into one or more modules/units, and the one or more modules/units are stored in the memory 20 and executed by the processor 30 , To complete the present invention. The one or more modules/units may be a series of computer program instruction segments capable of completing a specific function, and the instruction segments are used to describe the execution process of the computer program 40 in the computer device 1. For example, the computer program 40 can be divided into the acquisition module 101 and the calculation module 102 in FIG. 3.

所述電腦裝置1可以是桌上型電腦、筆記本、掌上型電腦及雲端伺服器等計算設備。本領域技術人員可以理解，所述示意圖僅僅是電腦裝置1之示例，並不構成對電腦裝置1之限定，可以包括比圖示更多或更少之部件，或者組合某些部件，或者不同之部件，例如所述電腦裝置1還可以包括輸入輸出設備、網路接入設備、匯流排等。 The computer device 1 may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server. Those skilled in the art can understand that the schematic diagram is only an example of the computer device 1, and does not constitute a limitation on the computer device 1. Components, for example, the computer device 1 may also include input and output devices, network access devices, bus bars, and the like.

所稱處理器30可以是中央處理單元(Central Processing Unit，CPU)，還可以是其他通用處理器、數位訊號處理器(Digital Signal Processor，DSP)、專用積體電路(Application Specific Integrated Circuit，ASIC)、現成可程式設計閘陣列(Field-Programmable Gate Array，FPGA)或者其他可程式設計邏輯器件、分立門或者電晶體邏輯器件、分立硬體元件等。通用處理器可以是微處理器或者所述處理器30也可以是任何常規之處理器等，所述處理器30是所述電腦裝置1之控制中心，利用各種介面和線路連接整個電腦裝置1之各個部分。 The so-called processor 30 may be a central processing unit (Central Processing Unit, CPU), other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), and dedicated integrated circuits (Application Specific Integrated Circuit, ASIC). , Ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor 30 can also be any conventional processor, etc. The processor 30 is the control center of the computer device 1 and connects the entire computer device 1 with various interfaces and lines. Various parts.

所述記憶體20可用於存儲所述電腦程式40和/或模組/單元，所述處理器30藉由運行或執行存儲在所述記憶體20內之電腦程式和/或模組/單元，以及調用存儲在記憶體20內之資料，實現所述電腦裝置1之各種功能。所述記憶體20可主要包括存儲程式區和存儲資料區，其中，存儲程式區可存儲作業系統、至少一個功能所需之應用程式(比如聲音播放功能、圖像播放功能等)等；存儲 資料區可存儲根據電腦裝置1之使用所創建之資料(比如音訊資料、電話本等)等。此外，記憶體20可以包括高速隨機存取記憶體，還可以包括非易失性記憶體，例如硬碟、記憶體、插接式硬碟，智慧存儲卡(Smart Media Card,SMC)，安全數位(Secure Digital,SD)卡，快閃記憶體卡(Flash Card)、至少一個磁碟記憶體件、快閃記憶體器件、或其他易失性固態記憶體件。 The memory 20 can be used to store the computer programs 40 and/or modules/units, and the processor 30 runs or executes the computer programs and/or modules/units stored in the memory 20, And call the data stored in the memory 20 to realize various functions of the computer device 1. The memory 20 may mainly include a storage program area and a storage data area. The storage program area may store an operating system, an application program required by at least one function (such as a sound playback function, an image playback function, etc.), etc.; The data area can store data (such as audio data, phone book, etc.) created based on the use of the computer device 1. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), and a secure digital device. (Secure Digital, SD) card, flash memory card (Flash Card), at least one magnetic disk memory device, flash memory device, or other volatile solid-state memory device.

所述電腦裝置1集成之模組/單元如果以軟體功能單元之形式實現並作為獨立之產品銷售或使用時，可以存儲在一個電腦可讀取存儲介質中。基於這樣之理解，本發明實現上述實施例方法中之全部或部分流程，也可以藉由電腦程式來指令相關之硬體來完成，所述之電腦程式可存儲於一電腦可讀存儲介質中，所述電腦程式在被處理器執行時，可實現上述各個方法實施例之步驟。其中，所述電腦程式包括電腦程式代碼，所述電腦程式代碼可以為原始程式碼形式、物件代碼形式、可執行檔或某些中間形式等。所述電腦可讀介質可以包括：能夠攜帶所述電腦程式代碼之任何實體或裝置、記錄介質、U盤、移動硬碟、磁碟、光碟、電腦記憶體、唯讀記憶體(ROM，Read-Only Memory)、隨機存取記憶體(RAM，Random Access Memory)、電載波信號、電信信號以及軟體分發介質等。需要說明之是，所述電腦可讀介質包含之內容可以根據司法管轄區內立法和專利實踐之要求進行適當之增減，例如在某些司法管轄區，根據立法和專利實踐，電腦可讀介質不包括電載波信號和電信信號。 If the integrated module/unit of the computer device 1 is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiments and methods, and can also be completed by computer programs instructing related hardware, and the computer programs can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the foregoing method embodiments. Wherein, the computer program includes computer program code, and the computer program code may be in the form of original program code, object code, executable file, or some intermediate forms. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only) Only Memory), Random Access Memory (RAM, Random Access Memory), electrical carrier signal, telecommunications signal, and software distribution media. It should be noted that the content contained in the computer-readable medium can be appropriately added or deleted according to the requirements of the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to the legislation and patent practice, the computer-readable medium Does not include electrical carrier signals and telecommunication signals.

在本發明所提供之幾個實施例中，應所述理解到，所揭露之電腦裝置和方法，可以藉由其它之方式實現。例如，以上所描述之電腦裝置實施例僅僅是示意性之，例如，所述單元之劃分，僅僅為一種邏輯功能劃分，實際實現時可以有另外之劃分方式。 In the several embodiments provided by the present invention, it should be understood that the disclosed computer device and method can be implemented in other ways. For example, the embodiments of the computer device described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other divisions in actual implementation.

另外，在本發明各個實施例中之各功能單元可以集成在相同處理單元中，也可以是各個單元單獨物理存在，也可以兩個或兩個以上單元集成在 相同單元中。上述集成之單元既可以採用硬體之形式實現，也可以採用硬體加軟體功能模組之形式實現。 In addition, the functional units in the various embodiments of the present invention may be integrated in the same processing unit, or each unit may exist alone physically, or two or more units may be integrated in the same processing unit. In the same unit. The above-mentioned integrated unit can be realized either in the form of hardware, or in the form of hardware plus software functional modules.

對於本領域技術人員而言，顯然本發明不限於上述示範性實施例之細節，而且在不背離本發明之精神或基本特徵之情況下，能夠以其他之具體形式實現本發明。因此，無論從哪一點來看，均應將實施例看作是示範性之，而且是非限制性之，本發明之範圍由所附申請專利範圍而不是上述說明限定，因此旨在將落在申請專利範圍之等同要件之含義和範圍內之所有變化涵括在本發明內。不應將申請專利範圍中之任何附圖標記視為限制所涉及之申請專利範圍。此外，顯然“包括”一詞不排除其他單元或步驟，單數不排除複數。電腦裝置申請專利範圍中陳述之多個單元或電腦裝置也可以由同一個單元或電腦裝置藉由軟體或者硬體來實現。第一，第二等詞語用來表示名稱，而並不表示任何特定之順序。 For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the scope of the appended patent application rather than the above description, so it is intended to fall within the application. The meaning of the equivalent elements of the patent scope and all changes within the scope are included in the present invention. Any reference signs in the scope of the patent application should not be regarded as limiting the scope of the patent application involved. In addition, it is obvious that the word "including" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or computer devices stated in the scope of the computer device patent application can also be implemented by the same unit or computer device by software or hardware. Words such as first and second are used to denote names, but do not denote any specific order.

最後應說明之是，以上實施例僅用以說明本發明之技術方案而非限制，儘管參照較佳實施例對本發明進行了詳細說明，本領域之普通技術人員應當理解，可以對本發明之技術方案進行修改或等同替換，而不脫離本發明技術方案之精神和範圍。 Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be Modifications or equivalent replacements are made without departing from the spirit and scope of the technical solutions of the present invention.

Claims (8)

一種災害保障資源計算方法，所述方法包括：獲取待評估場所之防災狀況資料，以及所述待評估場所在災害場景中之損失狀況資料，其中所述防災狀況資料中之專案包括環境狀態資訊、物品狀態資訊、人員數量；將所述防災狀況資料、損失狀況資料登錄到預設之計算模型中，輸出所述待評估場所在所述災害場景中所需之災害保障資源，其中，所述計算模型之訓練步驟包括：獲取不同場所之防災狀況資料、所述場所在災害場景中之損失狀況資料、所述場所在所述災害場景中所需之災害保障資源，並對每一所述場所之防災狀況資料和所述場所在災害場景中之損失狀況資料、所述場所在所述災害場景中所需之災害保障資源對應存儲，並將所述場所之防災狀況資料、損失狀況資料以及災害保障資料分為訓練集和驗證集；建立基於神經網路之計算模型，並利用所述訓練集對所述計算模型之參數進行訓練，其中將所述訓練集中之防災狀況資料、損失狀況資料作為所述計算模型之輸入資料，所述災害保障資料作為所述計算模型之輸出資料；利用所述驗證集對訓練後之計算模型進行驗證，並根據驗證結果統計得到所述計算模型預測準確率；判斷所述計算模型預測準確率是否小於預設閾值；若所述計算模型預測準確率不小於所述預設閾值，則結束對所述計算模型之訓練。 A method for calculating disaster protection resources, the method comprising: obtaining disaster prevention status data of a site to be evaluated, and loss status data of the site to be evaluated in a disaster scenario, wherein the items in the disaster prevention status data include environmental status information, Item status information, number of personnel; the disaster prevention status data and loss status data are registered in a preset calculation model, and the disaster protection resources required by the location to be assessed in the disaster scenario are output, wherein the calculation The training steps of the model include: obtaining the disaster prevention status data of different places, the loss status data of the places in the disaster scenes, the disaster protection resources required by the places in the disaster scenes, and analyzing the information of each place. Disaster prevention status data and the loss status data of the site in the disaster scene, the disaster protection resources required by the site in the disaster scene are correspondingly stored, and the disaster prevention status data, loss status data and disaster protection of the site The data is divided into a training set and a verification set; a neural network-based calculation model is established, and the training set is used to train the parameters of the calculation model, wherein the disaster prevention status data and the loss status data in the training set are used as the data The input data of the calculation model, the disaster protection data as the output data of the calculation model; use the verification set to verify the calculation model after training, and calculate the prediction accuracy of the calculation model according to the verification results; judge; Whether the prediction accuracy rate of the calculation model is less than the preset threshold; if the prediction accuracy rate of the calculation model is not less than the preset threshold, the training of the calculation model is ended. 如請求項1所述之災害保障資源計算方法，其中，獲取所述待評估場所在災害場景中之損失狀況資料之方法包括：獲取待評估場所之防災狀況資料，藉由災害數值模擬系統對所述待評估場 所進行不同災害場景下之模擬，並計算所述不同災害場景下所述待評估場所之損失狀況資料。 The disaster protection resource calculation method according to claim 1, wherein the method of obtaining the loss status data of the location to be assessed in the disaster scenario includes: obtaining the disaster prevention status data of the location to be assessed, and the disaster numerical simulation system Field to be evaluated Perform simulations under different disaster scenarios, and calculate the loss status data of the places to be assessed under the different disaster scenarios. 如請求項2所述之災害保障資源計算方法，其中，所述方法還包括：藉由災害預測模擬系統對所述待評估場所之防災狀況資料進行模擬，並判斷隨著所述防災狀況資料之變化是否會導致災害；其中，判斷是否會導致災害之方法包括：按照每種場所之防災狀況資料之變化範圍分為多個區間；按照所述區間之變化規律將所述每種場所之防災狀況資料按照不同之變化量依次輸入到災害預測模擬系統中；若所述資料之變化會觸發導致災害之條件，則確定所述資料能夠導致災害，其中導致災害之條件包括溫度、空氣中粉塵含量、空氣中有害氣體濃度。 The disaster protection resource calculation method according to claim 2, wherein the method further comprises: simulating the disaster prevention status data of the place to be evaluated by a disaster prediction simulation system, and judging that the disaster prevention status data follows Whether the change will cause a disaster; among them, the method of judging whether it will cause a disaster includes: dividing the disaster prevention status data into multiple sections according to the change range of the disaster prevention status data of each site; dividing the disaster prevention status of each site according to the change rule of the interval The data is sequentially input into the disaster prediction simulation system according to different changes; if the change in the data triggers the conditions that cause the disaster, it is determined that the data can cause the disaster. The conditions that cause the disaster include temperature, dust content in the air, The concentration of harmful gases in the air. 如請求項2所述之災害保障資源計算方法，其中，所述藉由災害數值模擬系統對所述待評估場所進行不同災害場景下之模擬，並計算所述不同災害場景下所述待評估場所之損失狀況資料之方法包括：設置所述待評估場所中每一物體在每一災害中之單位時間內之損失比例；按照預設比例對所述物體進行分割，分割後之每一區域代表所述物體在災害中單位時間內損失之最小金額；根據所述物體在每一災害中之單位時間內之損失比例、在火災中單位時間內損失之最小金額計算每一災害場景下所述場所之損失狀況資料。 The disaster guarantee resource calculation method according to claim 2, wherein the disaster numerical simulation system is used to simulate the place to be evaluated under different disaster scenarios, and calculate the place to be evaluated under the different disaster scenarios The method of the loss status data includes: setting the loss ratio of each object in the location to be assessed per unit time in each disaster; segmenting the object according to the preset ratio, and each area after the segmentation represents the The minimum amount of loss per unit time of the object in the disaster; the minimum amount of loss per unit time of the object in each disaster and the minimum amount of loss per unit time in the fire are calculated for each disaster scenario. Loss status information. 如請求項1所述之災害保障資源計算方法，其中，所述方法還包括：若所述計算模型預測準確率小於所述預設閾值，則調整所述計算模型之結構，並利用所述訓練集重新對調整後之計算模型進行訓練，其中，所述計算模 型之結構包括卷積核之數量、池化層中元素之數量、全連接層中元素之數量中之至少一種；利用所述驗證集對重新訓練之計算模型進行驗證，並根據驗證結果重新統計得到調整後之計算模型之模型預測準確率，並判斷調整後之計算模型之預測準確率是否小於所述預設閾值；若所述重新統計得到之模型預測準確率不小於所述預設閾值，則結束對所述計算模型之訓練；若所述重新統計得到之模型預測準確率小於所述預設閾值，則重複上述調整及訓練之步驟直至藉由所述驗證集驗證得到之模型預測準確率不小於所述預設閾值。 The disaster protection resource calculation method according to claim 1, wherein the method further includes: if the prediction accuracy rate of the calculation model is less than the preset threshold, adjusting the structure of the calculation model and using the training Set to retrain the adjusted calculation model, where the calculation model The type structure includes at least one of the number of convolution kernels, the number of elements in the pooling layer, and the number of elements in the fully connected layer; the verification set is used to verify the retrained calculation model, and re-count according to the verification results Obtain the model prediction accuracy rate of the adjusted calculation model, and determine whether the prediction accuracy rate of the adjusted calculation model is less than the preset threshold; if the model prediction accuracy rate obtained by the re-statistics is not less than the preset threshold, End the training of the calculation model; if the model prediction accuracy rate obtained by the re-statistics is less than the preset threshold, repeat the above adjustment and training steps until the model prediction accuracy rate is verified by the verification set Not less than the preset threshold. 一種災害保障資源計算裝置，所述裝置包括：獲取模組，用於獲取待評估場所之防災狀況資料，以及所述場所在災害場景中之損失狀況資料，其中所述防災狀況資料中之專案包括環境狀態資訊、物品狀態資訊、人員數量；計算模組，用於將所述防災狀況資料、損失狀況資料登錄到預設之計算模型中，輸出所述待評估場所在所述災害場景中所需之災害保障資料，其中，所述計算模組訓練計算模型，包括：獲取不同場所之防災狀況資料、所述場所在災害場景中之損失狀況資料、所述場所在所述災害場景中所需之災害保障資源，並對每一所述場所之防災狀況資料和所述場所在災害場景中之損失狀況資料、所述場所在所述災害場景中所需之災害保障資源對應存儲，並將所述場所之防災狀況資料、損失狀況資料以及災害保障資料分為訓練集和驗證集；建立基於神經網路之計算模型，並利用所述訓練集對所述計算模型之參數進行訓練，其中將所述訓練集中之防災狀況資料、損失狀況資料作為所述 計算模型之輸入資料，所述災害保障資料作為所述計算模型之輸出資料；利用所述驗證集對訓練後之計算模型進行驗證，並根據驗證結果統計得到所述計算模型預測準確率；判斷所述計算模型預測準確率是否小於預設閾值；若所述計算模型預測準確率不小於所述預設閾值，則結束對所述計算模型之訓練。 A computing device for disaster protection resources, the device comprising: an acquisition module for acquiring disaster prevention status data of a place to be assessed, and loss status data of the place in a disaster scenario, wherein the items in the disaster prevention status data include Environmental status information, item status information, and the number of personnel; a calculation module for registering the disaster prevention status data and loss status data into a preset calculation model, and outputting the location to be assessed in the disaster scenario Disaster protection data, wherein the calculation module training calculation model includes: obtaining data on the disaster prevention status of different places, the loss status data of the place in the disaster scene, and the information required by the place in the disaster scene Disaster protection resources, and correspondingly store the disaster prevention status data of each of the places and the loss status data of the places in the disaster scene, and the disaster protection resources required by the places in the disaster scene, and store the The site’s disaster prevention status data, loss status data, and disaster protection data are divided into a training set and a verification set; a neural network-based calculation model is established, and the training set is used to train the parameters of the calculation model, where the Disaster prevention status data and loss status data in the training set are used as the The input data of the calculation model, the disaster protection data is used as the output data of the calculation model; the calculation model after training is verified by the verification set, and the prediction accuracy rate of the calculation model is obtained by statistics according to the verification result; Whether the prediction accuracy rate of the calculation model is less than the preset threshold; if the prediction accuracy rate of the calculation model is not less than the preset threshold, the training of the calculation model is ended. 一種電腦裝置，所述電腦裝置包括處理器，所述處理器用於執行記憶體中存儲之電腦程式時實現如請求項1至5中任一項所述之災害保障資源計算方法。 A computer device, the computer device includes a processor, and the processor is used to implement the disaster protection resource calculation method described in any one of claim items 1 to 5 when the processor is used to execute a computer program stored in a memory. 一種電腦存儲介質，其上存儲有電腦程式，其中所述電腦程式被處理器執行時實現如請求項1至5中任一項所述之災害保障資源計算方法。 A computer storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the disaster protection resource calculation method as described in any one of claim items 1 to 5.
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