TWI745934B

TWI745934B - System, method and storage media for determining planting risk according to historical climate data and planting history

Info

Publication number: TWI745934B
Application number: TW109112883A
Authority: TW
Inventors: 林泰佑; 馮書昭; 江采蔚
Original assignee: 林泰佑
Priority date: 2020-04-16
Filing date: 2020-04-16
Publication date: 2021-11-11
Also published as: TW202141401A

Abstract

A system, a method and a storage media for determining a planting risk according to historical climate data and a planting history are provided. By obtaining historical climate data for a planting locality, reckoning a maturity date of the crop based on the historical climate data, determining disadvantages during growth of the crop in the light of the historical climate data, and determining planting risk in accordance with the disadvantages, the system and the method can effectively assess planting risk, and can achieve the effect of estimating harvest status.

Description

依據歷史氣候及種植歷史判斷種植風險之系統、方法及儲存媒體System, method and storage medium for judging planting risks based on historical climate and planting history

一種種植風險判斷系統、及其方法與儲存媒體，特別係指一種依據歷史氣候及種植歷史判斷種植風險之系統、方法、及儲存媒體。 A planting risk judgment system, its method, and storage medium, in particular, a system, method, and storage medium for judging planting risks based on historical climate and planting history.

傳統的農業大多依賴農務工作者的個人經驗來判斷生長環境的優劣對於作物生長週期等影響，進而依據作物的生長週期進行播種、施肥、耕作、灌溉、除蟲除草等種植作業，然而，僅憑個人經驗的種植方式並沒有辦法有效的控制收成的產量與品質。 Traditional agriculture mostly relies on the personal experience of agricultural workers to judge the impact of the quality of the growth environment on the growth cycle of crops, and then planting operations such as planting, fertilization, tillage, irrigation, and insecticide and weeding are carried out according to the growth cycle of the crop. The cultivation method of personal experience cannot effectively control the yield and quality of the harvest.

為此，有學者依據在標準環境生態下的累積資料統計出概括的作物的標準生長模型，以獲得理論上生長週期。然而，不同經緯度的產地之土壤的成分、溫度、濕度、及降雨量等環境生態條件均不相同，導致作物的標準模型無法為不同產地的相同作物精確計算具體的生長週期以及各生長階段的時程，導致收成的產量與品質依然無法有效控制，導致農務工作者在收成量差或收成品質不佳時，將無法獲利甚至賠本。 For this reason, some scholars have calculated a general standard growth model of crops based on accumulated data under standard environmental ecology to obtain a theoretical growth cycle. However, the environmental and ecological conditions such as soil composition, temperature, humidity, and rainfall in different latitudes and longitudes of production areas are not the same. As a result, the standard model of crops cannot accurately calculate the specific growth cycle and time of each growth stage for the same crop in different production areas. As a result, the yield and quality of the harvest cannot be effectively controlled. As a result, agricultural workers will not be able to make a profit or even lose money when the harvest is poor or the quality of the harvest is poor.

雖然有保險公司希望針對農務工作者提供相關的產品，使得農務工作者在收成量差或收成品質不佳導致無法獲利或賠本的情況下，仍然可以獲得讓來年繼續進行農務工作的收入。但目前並沒有能夠評估種植風險的有效機制，導致保險公司遲遲不願意提供給農務工作者的相關產品，且農務工作者也無法預期當年的收成狀況。 Although some insurance companies hope to provide related products for agricultural workers, agricultural workers can still obtain income to continue agricultural work in the coming year when the harvest is poor or poor quality results in no profit or loss. However, there is currently no effective mechanism for assessing planting risks, which has caused insurance companies to be reluctant to provide related products to agricultural workers, and agricultural workers cannot predict the harvest situation that year.

綜上所述，可知先前技術中長期以來一直存在無法有效評估種植風險的問題，因此有必要提出改進的技術手段，來解決此一問題。 In summary, it can be seen that there has been a problem in the prior art that the planting risk cannot be effectively assessed for a long time. Therefore, it is necessary to propose improved technical means to solve this problem.

有鑒於先前技術存在無法有效評估種植風險的問題，本發明遂揭露一種依據歷史氣候及種植歷史判斷種植風險之系統、方法、及儲存媒體，其中： In view of the problem that the prior art cannot effectively assess the planting risk, the present invention discloses a system, method, and storage medium for judging planting risks based on historical climate and planting history, in which:

本發明所揭露之依據歷史氣候及種植歷史判斷種植風險之系統，至少包含：目標設定模組，用以設定目標作物、種植地點、種植日期；資料取得模組，用以取得種植地點之歷史氣候資料；生長推算模組，用以依據歷史氣候資料推算目標作物之成熟日期；風險判斷模組，用以依據歷史氣候資料判斷目標作物由種植日期到成熟日期間之不利因素，並依據不利因素判斷種植風險。 The system for judging planting risks based on historical climate and planting history disclosed in the present invention at least includes: a target setting module for setting target crops, planting locations, and planting dates; and a data acquisition module for acquiring the historical climate of planting locations Data; growth estimation module, used to estimate the maturity date of the target crop based on historical climate data; risk judgment module, used to determine the unfavorable factors of the target crop from the planting date to the mature date based on historical climate data, and judge based on the unfavorable factors Planting risk.

本發明所揭露之依據歷史氣候及種植歷史判斷種植風險之方法，其步驟至少包括：設定目標作物、種植地點、及種植日期；取得種植地點之歷史氣候資料；依據歷史氣候資料推算目標作物之成熟日期；依據歷史氣候資料判斷目標作物由種植日期到成熟日期間之不利因素；依據不利因素判斷種植風險。 The method for judging planting risks based on historical climate and planting history disclosed in the present invention includes at least the steps of: setting target crops, planting locations, and planting dates; and obtaining planting locations Calculate the maturity date of the target crop based on the historical climate data; determine the unfavorable factors of the target crop from the planting date to the mature date based on the historical climate data; determine the planting risk based on the unfavorable factors.

本發明所揭露之儲存媒體，儲存有電腦程式，當電腦程式被處理器執行時，可以實現上述之依據歷史氣候及種植歷史判斷種植風險之方法。 The storage medium disclosed in the present invention stores a computer program. When the computer program is executed by a processor, the above-mentioned method of judging planting risks based on historical climate and planting history can be realized.

本發明所揭露之系統與方法如上，與先前技術之間的差異在於本發明透過在取得種植地點之歷史氣候資料後，依據歷史氣候資料推算目標作物之成熟日期，並依據歷史氣候資料判斷目標作物由種植日期到成熟日期間之不利因素，及依據不利因素判斷種植風險，藉以解決先前技術所存在的問題，並可以達成預估收成狀況的技術功效。 The system and method disclosed in the present invention are as above. The difference with the prior art is that the present invention calculates the maturity date of the target crop based on the historical climate data after obtaining the historical climate data of the planting site, and judges the target crop based on the historical climate data The unfavorable factors during the period from the planting date to the maturity date, and the planting risks are judged according to the unfavorable factors, so as to solve the problems of the previous technology and achieve the technical effect of predicting the harvest status.

110:目標設定模組 110: Goal setting module

120:資料取得模組 120: Data Acquisition Module

130:生長推算模組 130: Growth calculation module

150:風險判斷模組 150: Risk Judgment Module

400:伺服器 400: server

步驟210:設定目標作物、種植地點、種植日期 Step 210: Set target crops, planting locations, and planting dates

步驟220:取得種植地點之歷史氣候資料 Step 220: Obtain historical climate data of the planting site

步驟230:依據歷史氣候資料推算目標作物之成熟日期 Step 230: Calculate the maturity date of the target crop based on historical climate data

步驟231:取得種植地點之歷史種植記錄 Step 231: Obtain the historical planting record of the planting site

步驟235:依據歷史氣候資料及歷史種植記錄中目標作物之多個生長階段之生長天數及各生長階段之環境生態資料建立生長模型 Step 235: Establish a growth model based on the historical climate data and the growth days of multiple growth stages of the target crop in the historical planting records and the environmental and ecological data of each growth stage

步驟239:使用生長模型推算成熟日期 Step 239: Use the growth model to estimate the maturity date

步驟250:依據歷史氣候資料判斷目標作物由種植日期到成熟日期間之不利因素 Step 250: Determine the unfavorable factors of the target crop from the planting date to the mature date based on historical climate data

步驟251:依據歷史氣候資料中每一年由種植日期到成熟日期間之氣候資料判斷目標作物由種植日期到成熟日期間之各生長階段之風險分數 Step 251: Determine the risk score of each growth stage of the target crop from the planting date to the maturity date based on the climate data from the planting date to the maturity date in each year in the historical climate data

步驟253:計算種植地點在種植日期前預定日期至種植日期間之天氣資料與歷史氣候資料的相似度 Step 253: Calculate the similarity between the weather data and historical climate data of the planting site from the scheduled date before the planting date to the planting date

步驟253:依據相似度由歷史氣候資料中選出相近氣候資料 Step 253: Select similar climate data from historical climate data based on similarity

步驟255:依據相近氣候資料推測種植日期至成熟日期間之預估環境資訊 Step 255: Estimate the estimated environmental information from the planting date to the mature date based on similar climatic data

步驟257:依據相似度對各風險分數加權以計算各生長階段之不利因素 Step 257: Weight the risk scores according to the similarity to calculate the unfavorable factors of each growth stage

步驟260:依據不利因素判斷種植風險 Step 260: Determine planting risks based on unfavorable factors

第1圖為本發明所提之依據歷史氣候及種植歷史判斷種植風險之系統架構圖。 Figure 1 is a diagram of the system architecture for judging planting risks based on historical climate and planting history according to the present invention.

第2A圖為本發明所提之依據歷史氣候及種植歷史判斷種植風險之方法流程圖。 Figure 2A is a flow chart of the method for judging planting risks based on historical climate and planting history according to the present invention.

第2B圖為本發明所提之判斷目標作物生長期之不利因素之方法流程圖。 Figure 2B is a flow chart of the method for judging the unfavorable factors of the target crop's growth period according to the present invention.

第2C圖為本發明所提之依據不利因素判斷種植風險之方法流程圖。 Figure 2C is a flow chart of the method for judging planting risks based on unfavorable factors according to the present invention.

以下將配合圖式及實施例來詳細說明本發明之特徵與實施方式，內容足以使任何熟習相關技藝者能夠輕易地充分理解本發明解決技術問題所應用的技術手段並據以實施，藉此實現本發明可達成的功效。 In the following, the features and implementation of the present invention will be described in detail with the drawings and embodiments. The content is sufficient to enable any person familiar with the relevant art to easily and fully understand the technical means used by the present invention to solve the technical problems and implement them accordingly. The achievable effect of the present invention.

本發明可以依據目標作物之種植地點過往的氣候資料推算目標作物可能的成熟日期，進而判斷目標作物在生長期(也就是種植日期至成熟日期)內的不利因素。 The present invention can calculate the possible maturity date of the target crop based on the past climate data of the planting location of the target crop, and then judge the unfavorable factors of the target crop in the growth period (that is, the planting date to the mature date).

其中，本發明所提之氣候資料包含氣溫、氣壓、太陽輻射量、日射量、濕度、風速、降雨量等氣候資料，在部分的實施例中，還可以包含颱風、豪雨等災害資料，但本發明並不以此為限。本發明所提之不利因素包含但不限於高/低溫之次數、高/低溫量、高降雨次數、高降雨日數、日照不足日數、日照不足量、土壤濕度過高/低之次數、土壤/空氣元素成分過低之次數等，在部分的實施例中，不利因素還可以包含種植者的農務工作方式所造成的影響，例如，耕種/灌溉方式是否適合目標作物、肥料使用狀況是否合理等。 Among them, the climate data mentioned in the present invention includes climate data such as temperature, air pressure, solar radiation, insolation, humidity, wind speed, rainfall, etc. In some embodiments, it may also include disaster data such as typhoon and heavy rain. The invention is not limited to this. The unfavorable factors mentioned in the present invention include, but are not limited to, the frequency of high/low temperature, the amount of high/low temperature, the number of high rainfall, the number of days of high rainfall, the number of days with insufficient sunshine, the amount of insufficient sunshine, the number of times the soil moisture is too high/low, the number of soil moisture /The number of times the air element composition is too low, etc. In some embodiments, the unfavorable factors may also include the impact of the grower’s agricultural work methods, such as whether the cultivation/irrigation method is suitable for the target crop, whether the fertilizer usage is reasonable, etc. .

以下先以「第1圖」本發明所提之依據歷史氣候及種植歷史判斷種植風險之系統架構圖來說明本發明的系統運作。如「第1圖」所示，本發明之系統含有目標設定模組110、資料取得模組120、生長推算模組130、風險判斷模組150。 Hereinafter, the system structure diagram of the system for judging planting risks based on historical climate and planting history mentioned in the "Figure 1" of the present invention is used to illustrate the operation of the system of the present invention. As shown in "Figure 1", the system of the present invention includes a target setting module 110, a data acquisition module 120, a growth estimation module 130, and a risk judgment module 150.

目標設定模組110負責設定目標作物、種植地點、與種植日期。目標設定模組110可以提供使用者介面給使用者，並依據使用者所輸入的資料設定目標作物、種植地點、與種植日期。 The target setting module 110 is responsible for setting target crops, planting locations, and planting dates. The target setting module 110 can provide a user interface to the user, and set the target crop, planting location, and planting date according to the data input by the user.

資料取得模組120負責取得目標設定模組110所設定之種植地點的歷史氣候資料。資料取得模組120所取得的歷史氣候資料包含了種植地點過去數年至數十年的氣候資料。 The data obtaining module 120 is responsible for obtaining historical climate data of the planting site set by the target setting module 110. The historical climate data acquired by the data acquisition module 120 includes the climate data of the planting site in the past several years to decades.

一般而言，資料取得模組120可以連線至外部的伺服器400下載目標設定模組110所設定之種植地點的歷史氣候資料，但本發明並不以此為限，例如，資料取得模組120也可以先由外部的伺服器400下載所有地點的歷史氣候資料，再讀出目標設定模組110所設定之種植地點的歷史氣候資料。上述之外部的伺服器400包含但不限於政府機關、企業、或個人所提供的天氣伺服器或資料伺服器，如氣象局所提供的伺服器等，其中，個人所提供的資料伺服器可以包含特別收集或記錄的氣候資料。 Generally speaking, the data acquisition module 120 can connect to an external server 400 to download the historical climate data of the planting site set by the target setting module 110, but the present invention is not limited to this, for example, the data acquisition module 120 can also download the historical climate data of all locations from the external server 400 first, and then read out the historical climate data of the planting locations set by the target setting module 110. The aforementioned external server 400 includes, but is not limited to, weather servers or data servers provided by government agencies, enterprises, or individuals, such as servers provided by the Meteorological Bureau. Among them, the data servers provided by individuals may include special Climate data collected or recorded.

資料取得模組120也可以取得目標設定模組110所設定之目標作物於種植地點的歷史種植記錄。資料取得模組120所取得的歷史種植記錄包含了種植地點及/或周圍地區在過去數年種植目標作物的種植資料，種植資料包含種植日期、目標作物各生長階段的時間區間(各個生長階段的生長天數)、收成日期、收成量(產量)、收成品質等項目，在部分的實施例中，種植資料還可以包含種植歷程，也就是記錄種植時所使用的耕作、施肥、灌溉、除蟲等農務工作的方式與日期，甚至，種植資料還可以包含環境生態資料，例如土壤資料及環境資料等，其中，土壤資料包含但不限於種植地點的土壤溫度、土壤濕度(土壤含水量)、土壤導電度、土壤酸鹼度、土壤元素成分等，環境資料包含但不限於種植地點的坡度、空氣元素成分等。但種植資料所包含的項目並不以上述為限。 The data obtaining module 120 can also obtain the historical planting records of the target crop at the planting location set by the target setting module 110. The historical planting records obtained by the data acquisition module 120 include planting data of the planting location and/or surrounding areas for planting the target crops in the past few years. Growth days), harvest date, harvest volume (yield), harvest quality and other items. In some embodiments, the planting data may also include the planting process, that is, to record the tillage, fertilization, irrigation, and deworming used during planting. The method and date of agricultural work, and even the planting data can also include environmental and ecological data, such as soil data and environmental data. Among them, the soil data includes, but is not limited to, the soil temperature, soil moisture (soil water content), and soil conductivity at the planting site. The environmental information includes, but is not limited to, the slope of the planting site, the air element composition, etc. However, the items included in the planting materials are not limited to the above.

相似的，資料讀取模組120可以讀取農務工作者所記錄的歷史種植記錄，但本發明並不以此為限，例如，資料取得模組120也可以連線至外部的伺服器400下載目標作物於種植地點的歷史種植記錄。上述之外部的伺服器400包含但不限於政府機關、企業、或私人所提供的資料伺服器。 Similarly, the data reading module 120 can read historical planting records recorded by agricultural workers, but the present invention is not limited to this. For example, the data acquisition module 120 can also be connected to an external server 400 to download The historical planting record of the target crop at the planting location. The aforementioned external server 400 includes, but is not limited to, a data server provided by government agencies, enterprises, or private individuals.

需要說明的是，種植資料所包含的部分項目也可能是由資料取得模組120計算產生，例如，資料取得模組120可以依據種植地點在生長期中之特定日期的前30天與前180天的平均值雨量計算正常降水分數，並依據種植地點之坡度計算排水量，並依據正常降雨分數與排水量評估土壤濕度。 It should be noted that some of the items included in the planting data may also be calculated by the data acquisition module 120. For example, the data acquisition module 120 can be based on the planting location 30 days before and 180 days before the specific date in the growth period. Calculate the normal rainfall score based on the average rainfall, and calculate the drainage volume based on the slope of the planting site, and evaluate the soil moisture based on the normal rainfall score and drainage volume.

生長推算模組130負責依據資料取得模組120所取得之種植地點的歷史氣候資料推算目標作物的成熟日期。 The growth estimation module 130 is responsible for calculating the maturity date of the target crop based on the historical climate data of the planting site obtained by the data obtaining module 120.

在部分的實施例中，生長推算模組130可以先依據資料取得模組120所取得的歷史氣候資料及歷史種植記錄中之目標作物之不同生長階段的生長天數以及各個生長階段的一個或多個環境生態資料建立生長模型，再將目標設定模組110所設定之種植日期輸入生長模型，使得生長模型可以依據歷史氣候資料及歷史種植記錄推算出在種植日期種植之目標作物的各個生長階段所需要的生長天數及成熟日期等資料。 In some embodiments, the growth calculation module 130 may first base on the historical climate data obtained by the data acquisition module 120 and the growth days of the target crops in different growth stages in the historical planting records and one or more of each growth stage. A growth model is established based on environmental and ecological data, and then the planting date set by the target setting module 110 is input into the growth model, so that the growth model can calculate the needs of each growth stage of the target crop planted on the planting date based on historical climate data and historical planting records The number of growth days and maturity date and other information.

風險判斷模組150負責依據資料取得模組120所取得的歷史氣候資料判斷目標設定模組110所設定之目標作物在生長期(種植日期到成熟日期)間的不利因素。 The risk judgment module 150 is responsible for judging the unfavorable factors of the target crop set by the target setting module 110 during the growth period (planting date to mature date) based on the historical climate data obtained by the data obtaining module 120.

更詳細的，風險判斷模組150可以依據歷史氣候資料中每一年由種植日期到成熟日期間之氣候資料判斷目標作物由種植日期到成熟日期間的風險分數，並可以計算種植地點在種植日期前一預定日期至種植日期間的天氣資料與歷史氣候資料的相似度，再依據所計算出之相似度對各該風險分數加權以計算不利因素。其中，風險判斷模組150可以依據目標作物在不同生長階段之各種不利因素的影響時間與影響程度分別計算各生長階段在不同年度的推定不利分數，並依據推定不利分數判斷各生長階段的風險分數。 In more detail, the risk judgment module 150 can judge the risk score of the target crop from the planting date to the maturity date based on the climate data from the planting date to the mature date in each year in the historical climate data, and can calculate the planting location on the planting date. Weather information from the previous scheduled date to the planting date According to the similarity of historical climate data, the risk scores are weighted according to the calculated similarity to calculate the unfavorable factors. Among them, the risk judgment module 150 can calculate the estimated disadvantage scores of each growth stage in different years according to the influence time and degree of influence of various unfavorable factors at different growth stages of the target crop, and judge the risk scores of each growth stage according to the estimated disadvantage scores. .

風險判斷模組150計算天氣資料與歷史氣候資料之相似度的方式例如將當年氣候資料中，氣溫、濕度以及氣壓等項目整理成一個多維度的向量表A，將種植地點過去數年在同一時間的項目整理成相同維度的向量表B1、B2、B3、B4、…、Bn，並可以使用餘弦相似性(cosine similarity)來計算當年的向量表A與之前年份的向量表Bi(i=1~n)的相似度，其中，可以使用平滑化(採樣)、正規化(變成0~1)、計算新參數(如月平均差)等方式進行調整來產生向量表，也就是說，並不一定要直接使用原始的氣候資料產生向量表。另外，當年度的氣候資料也可以使用歷年的平均分數及分數散佈情形來預估。但相似度的計算方式、產生向量表的方式、及當年度天氣的預估方式都不以上述為限。 The risk judgment module 150 calculates the similarity between weather data and historical climate data. For example, the temperature, humidity, and air pressure in the climate data of the current year are sorted into a multi-dimensional vector table A, and the planting locations are at the same time in the past few years. The items are organized into vector tables B1, B2, B3, B4,..., Bn of the same dimensions, and cosine similarity can be used to calculate the vector table A of the current year and the vector table Bi (i=1~ The similarity of n), in which, smoothing (sampling), normalization (to 0~1), calculation of new parameters (such as monthly average difference) can be used to adjust to generate a vector table, that is, it is not necessary Directly use the original climate data to generate the vector table. In addition, the climate data of the current year can also be estimated using the average score and the distribution of scores over the years. However, the calculation method of similarity, the method of generating the vector table, and the method of predicting the weather of the current year are not limited to the above.

在部分的實施例中，風險判斷模組150也可以依據預先設定的權重調整目標作物在不同生長階段的風險分數。例如，大豆在生長期間共會經歷營養生長期、開花期、莢及籽粒生長期、及莢及籽粒成熟期等四個生長階段，其中以開花期對於「日照量過多」的不利因素最為敏感，但「日照量過多」的不利因素對其他生長階段而言影響不大，如此，風險判斷模組150可以增加開花期的「日照量過多」之不利條件的權重，使得大豆在開花期發生「日照量過多」之不利因素時所反映出的風險分數較高。相反的，風險判斷模組150也可以降低非開花期的「日照量過多」之不利條件的權重。 In some embodiments, the risk judgment module 150 can also adjust the risk scores of the target crop in different growth stages according to preset weights. For example, soybeans undergo four growth stages during the growth period: vegetative growth, flowering, pod and seed growth, and pod and seed maturity. Among them, the flowering period is most sensitive to the adverse factors of "excessive sunlight". However, the unfavorable factors of "excessive sunshine" have little effect on other growth stages. In this way, the risk judgment module 150 can increase the weight of the disadvantages of "excessive sunshine" during the flowering period, so that soybeans will experience "sunshine" during the flowering period. The unfavorable factors of “excessive amount” reflect a higher risk score. Conversely, the risk judgment module 150 can also reduce the weight of the unfavorable condition of "excessive sunshine" in the non-flowering period.

其中，風險判斷模組150可以參考專家意見來定義各個生長階段之不利因素的權重，也可以根據生長期的性狀特徵變化及環境資料進行特徵分析來定義各個生長階段之不利因素的權重。舉例來說，根據研究，大豆在營養生長期的生長階段中，若土壤含水量在50~60%且土壤溫度在20~22度，則具有最高的發芽率，但若土壤含水量或土壤溫度改變便會造成發芽率或收成品質下降。更詳細的，就土壤含水量而言，最佳的比率為50~60%，若高於60%，則土壤過於潮濕，通氣性不佳，容易造成氧氣不足而導致發芽不良，甚至容易造成種子感染黴菌而腐敗，如此將降低收成品質；若低於50%，則土壤過於乾燥，則種子出土困難，如此將造成發芽不良減少收成量。因此，風險判斷模組150可以定義土壤含水量過高或過低、及土壤溫度過高或過低為大豆在營養生長期的不利因素，且風險判斷模組150可以根據對收成影響的嚴重程度設定不同的權重，例如，可以對土壤含水量設定45%以下、45~50%、50~60%、及60%以上等四個區間，並根據大豆之營養生長期處在各個區間的時間長短計算營養生長期的風險分數。 Among them, the risk judgment module 150 can refer to expert opinions to define the weights of unfavorable factors in each growth stage, and can also perform feature analysis based on changes in traits during the growth period and environmental data to define the weights of unfavorable factors in each growth stage. For example, according to research, soybeans have the highest germination rate if the soil moisture content is 50-60% and the soil temperature is 20-22 degrees in the vegetative growth phase, but if the soil moisture content or soil temperature Changes will cause the germination rate or harvest quality to decline. In more detail, in terms of soil water content, the best ratio is 50-60%. If it is higher than 60%, the soil will be too moist and poorly aerated, which will easily lead to insufficient oxygen and lead to poor germination and even seeds. Infected with mold and decayed, this will reduce the quality of the harvest; if it is less than 50%, the soil is too dry, and the seeds are difficult to unearth, which will cause poor germination and reduce the yield. Therefore, the risk judgment module 150 can define that the soil moisture content is too high or too low, and the soil temperature is too high or too low as unfavorable factors in the vegetative growth period of soybeans, and the risk judgment module 150 can be based on the severity of the impact on the harvest. Set different weights. For example, you can set four intervals for soil moisture content below 45%, 45~50%, 50~60%, and more than 60%, and according to the length of time that soybeans are in each zone during the vegetative growth period Calculate the risk score during the vegetative growth period.

另外，風險判斷模組150也可以依據資料取得模組120所取得的歷史氣候資料判斷判斷目標作物之生長期內發生災害的機率以調整目標作物在不同生長階段的風險分數。 In addition, the risk judgment module 150 can also judge the probability of disasters during the growth period of the target crop based on the historical climate data obtained by the data acquisition module 120 to adjust the risk scores of the target crop in different growth stages.

接著以一個實施例來解說本發明的運作系統與方法，並請參照「第2A圖」本發明所提之依據歷史氣候及種植歷史判斷種植風險之方法流程圖。在本實施例中，假設本發明包含在保險公司的伺服器或保險業務的手機或平板電腦所安裝的電腦程式中，但本發明並不以此為限。 Next, an embodiment is used to explain the operating system and method of the present invention, and please refer to the flowchart of the method for judging planting risks based on historical climate and planting history mentioned in "Figure 2A" of the present invention. In this embodiment, it is assumed that the present invention is included in a computer program installed on a server of an insurance company or a mobile phone or tablet computer of an insurance business, but the present invention is not limited to this.

當保險業務或保險公司要判斷是否可以給目標作物承保時，可以執行包含本發明的電腦程式，使得目標設定模組110設定目標作物、種植地點、及種植日期(步驟210)。在本實施例中，假設目標設定模組110可以提供使用者介面，使得保險業務或保險公司的員工可以在使用者介面中選擇目標作物為大豆、種植地點為花蓮縣光復鄉大豐村、種植日期為3/14，目標設定模組110可以取得並設定保險業務或保險公司的員工所選擇的目標作物、種植地點、及種植日期。 When the insurance business or the insurance company wants to determine whether the target crop can be underwritten, the computer program including the present invention can be executed, so that the target setting module 110 sets the target crop, planting location, and planting date (step 210). In this embodiment, it is assumed that the target setting module 110 can provide a user interface so that employees of an insurance business or insurance company can select the target crop as soybeans, and the planting location is Dafeng Village, Guangfu Township, Hualien County in the user interface. The date is 3/14, and the target setting module 110 can obtain and set the target crops, planting locations, and planting dates selected by the insurance business or employees of the insurance company.

在目標設定模組110設定目標作物、種植地點、及種植日期(步驟210)後，資料取得模組120可以取得種植地點的歷史氣候資料(步驟220)。在本實施例中，假設資料取得模組120可以透過氣象局所提供的API連線到氣象局所提供的伺服器400下載目標設定模組110所設定之種植地點的歷史氣候資料。 After the target setting module 110 sets the target crop, planting location, and planting date (step 210), the data obtaining module 120 can obtain historical climate data of the planting site (step 220). In this embodiment, it is assumed that the data acquisition module 120 can connect to the server 400 provided by the Meteorological Bureau through the API provided by the Meteorological Bureau to download the historical climate data of the planting site set by the target setting module 110.

在資料取得模組120取得目標設定模組110所設定之種植地點的歷史氣候資料(步驟220)後，生長推算模組130可以依據資料取得模組120所取得的歷史氣候資料推算目標設定模組110所設定之目標作物的成熟日期(步驟230)。在本實施例中，假設如「第2B圖」的流程所示，資料取得模組120可以在取得歷史氣候資料(步驟220)後，還可以透過農委會提供的資訊服務網取得目標作物於種植地點的歷史種植記錄(步驟231)，生長推算模組130可以依據資料取得模組120所取得的歷史氣候資料及歷史種植記錄中目標作物之多個生長階段之生長天數及各生長階段之環境生態資料建立生長模型(步驟235)，並依據種植日期使用所建立的生長模型推算目標作物的可能成熟日期(步驟239)，例如7/6。 After the data obtaining module 120 obtains the historical climate data of the planting location set by the target setting module 110 (step 220), the growth estimation module 130 may calculate the target setting module based on the historical climate data obtained by the data obtaining module 120 The maturity date of the target crop set by 110 (step 230). In this embodiment, it is assumed that as shown in the process in "Figure 2B", the data acquisition module 120 can obtain the historical climate data (step 220), and can also obtain the target crops through the information service network provided by the Council of Agriculture. The historical planting record of the planting site (step 231), the growth calculation module 130 can obtain the historical climate data obtained by the data acquisition module 120 and the growth days of the target crop in multiple growth stages and the environment of each growth stage in the historical planting record A growth model is established based on the ecological data (step 235), and the established growth model is used to calculate the possible maturity date of the target crop according to the planting date (step 239), for example, 7/6.

在生長推算模組130推算出目標設定模組110所設定之目標作物的成熟日期(步驟230)後，風險判斷模組150可以依據資料取得模組120所取得的歷史氣候資料判斷目標作物由種植日期到成熟日期間的不利因素(步驟250)。在本實施例中，假設風險判斷模組150可以如「第2C圖」所示之流程，先依據歷史氣候資料中每一年在種植日期到成熟日期間之氣候資料判斷目標設定模組110所設定之目標作物由種植日期到成熟日期間之各生長階段的風險分數(步驟251)，例如，若大豆在營養生長期的不利因素觀測計算表如下：

當推算的營養生長期為30天(720小時)，且歷史氣候資料中之2017年在推算出之營養生長期的同一時間的土壤含水量之各個觀測值域所佔的比例為36小時低於45%、144小時在45%~50%之間、468小時在50%~60%之間、72小時高於60%，則大豆在營養生長期中對2017年之土壤含水量的風險分數為612(36*5+144*2+468*0+72*2)。相似的，對2018年之土壤含水量的風險分數為482、對2019年之土壤含水量的風險分數為918。 After the growth calculation module 130 calculates the maturity date of the target crop set by the target setting module 110 (step 230), the risk judgment module 150 can determine that the target crop is planted based on the historical climate data obtained by the data acquisition module 120 Unfavorable factors from date to maturity date (step 250). In this embodiment, it is assumed that the risk judgment module 150 can be as shown in the process shown in "Figure 2C". Set the risk score of each growth stage of the target crop from the planting date to the maturity date (step 251). For example, if soybeans are in the vegetative growth period, the observation and calculation table of unfavorable factors is as follows:

When the estimated vegetative growth period is 30 days (720 hours), and the proportion of each observation range of soil water content at the same time of the estimated vegetative growth period in 2017 in the historical climate data is 36 hours lower than 45%, 144 hours are between 45% and 50%, 468 hours are between 50% and 60%, and 72 hours are higher than 60%. So the risk score of soybeans to the soil water content in 2017 during the vegetative growth period is 612(36*5+144*2+468*0+72*2). Similarly, the risk score for soil water content in 2018 is 482, and the risk score for soil water content in 2019 is 918.

在風險判斷模組150依據歷史氣候資料中每一年在種植日期到成熟日期間之氣候資料判斷目標設定模組110所設定之目標作物由種植日期到成熟日期間之風險分數(步驟251)後，風險判斷模組150可以計算種植地點在種植日期前之預定日期至種植日期間之天氣資料與歷史氣候資料的相似度(步驟253)，並依據相似度對各該風險分數加權以計算目標作物在各個生長階段之不利因素(步驟257)，也就是比對各生長階段的適宜環境資料及預估環境資料以判斷目標作物在各個生長階段之不利因素。例如，風險判斷模組150可以在計算出2017~2019年之氣候與當年之氣候的相似度分別為60%、20%、20%時，以相似度作為權重對2017~2019的風險分數加權，並計算加權後的總合，也就是說，當年的不利因素為647.2(612*0.6+482*0.2+918*0.2)。在風險判斷模組150判斷目標作物由種植日期到成熟日期間的不利因素(步驟250)後，可以依據不利因素判斷種植風險(步驟260)。在本實施例中，假設風險判斷模組150可以加總各個生長階段的風險分數，並判斷是否有任何一個生長階段的風險分數高於拒保門檻值(如1500)，若是，則表示種植風險過高，不建議保險公司承保，若否，則風險判斷模組150可以判斷是否有任何一個生長階段的風險分數高於減額門檻值(如500)，若是，則表示種植風險較高，保險公司應以較低的理賠金承保，若否，表示種植風險在合理範圍，保險公司可以較高的理賠金承保。 After the risk judgment module 150 judges the risk score of the target crop from the planting date to the mature date set by the target setting module 110 based on the climate data from the planting date to the mature date in each year in the historical climate data (step 251) , The risk judgment module 150 can calculate the similarity between the weather data and the historical climate data of the planting site from the scheduled date before the planting date to the planting date (step 253), and weight each risk score according to the similarity to calculate the target crop The unfavorable factors in each growth stage (step 257), that is, compare the suitable environmental data and estimated environmental data in each growth stage to judge the unfavorable factors of the target crop in each growth stage. For example, the risk judgment module 150 can use the similarity as a weight to weight the 2017-2019 risk scores when calculating the similarity between the 2017-2019 climate and the current climate as 60%, 20%, and 20%, respectively. And calculate the weighted total, that is, the unfavorable factor of the year is 647.2 (612*0.6+482*0.2+918*0.2). After the risk judgment module 150 judges the unfavorable factors of the target crop from the planting date to the mature date (step 250), the planting risk can be judged according to the unfavorable factors (step 260). In this embodiment, it is assumed that the risk judgment module 150 can add up the risk scores of each growth stage, and determine whether the risk score of any growth stage is higher than the rejection threshold (such as 1500). If so, it indicates the planting risk. If it is too high, the insurance company is not recommended to underwrite. If not, the risk judgment module 150 can determine whether the risk score of any growth stage is higher than the derating threshold (such as 500). If it is, it means that the planting risk is high, and the insurance company It should be underwritten with a lower claim fee. If not, it means that the planting risk is within a reasonable range, and the insurance company can underwrite it with a higher claim fee.

如此，透過本發明判斷目標作物在種植地點的種植風險，保險公司或保險業務可以依據本發明所判斷出之種植風險了解是否能夠承保及如何選擇合適的理賠金額。 In this way, by judging the planting risk of the target crop at the planting site through the present invention, the insurance company or insurance business can understand whether the insurance can be underwritten and how to choose the appropriate claim amount based on the planting risk determined by the present invention.

綜上所述，可知本發明與先前技術之間的差異在於具有在取得種植地點之歷史氣候資料後，依據歷史氣候資料推算目標作物之成熟日期，並依據歷史氣候資料判斷目標作物由種植日期到成熟日期間之不利因素，及依據不利因素判斷種植風險之技術手段，藉由此一技術手段可以解決先前技術所存在無法有效評估種植風險的問題，進而達成預估收成狀況的技術功效。 In summary, it can be seen that the difference between the present invention and the prior art is that after obtaining the historical climate data of the planting site, it is possible to calculate the maturity date of the target crop according to the historical climate data and According to historical climate data, the unfavorable factors of the target crop from the planting date to the maturity date are judged, and the planting risk is judged according to the unfavorable factors. This technology can solve the problem of the previous technology that cannot effectively assess the planting risk, and then The technical effect of achieving the estimated harvest status.

再者，本發明之依據歷史氣候及種植歷史判斷種植風險之方法，可實現於硬體、軟體或硬體與軟體之組合中，亦可在電腦系統中以集中方式實現或以不同元件散佈於若干互連之電腦系統的分散方式實現。 Furthermore, the method of judging planting risks based on historical climate and planting history of the present invention can be implemented in hardware, software, or a combination of hardware and software, and can also be implemented in a centralized manner in a computer system or distributed in different components. Several interconnected computer systems are implemented in a decentralized manner.

另外，本發明還提供一種電腦可讀之儲存媒體(亦可簡稱為可讀儲存媒體)，其上儲存有電腦程式，當電腦程式被處理器執行時可以實現上述實施利所提之依據歷史氣候及種植歷史判斷種植風險之方法。 In addition, the present invention also provides a computer-readable storage medium (also referred to as a readable storage medium for short), on which a computer program is stored, and when the computer program is executed by a processor, it can realize the aforementioned implementation benefits based on historical climate. And the method of planting history to judge planting risk.

本領域普通技術人員可以理解電腦可讀之儲存媒體為：實現上述各方法實施例的全部或部分步驟可以通過電腦程式相關的硬體來完成。前述的電腦程式可以儲存於電腦可讀儲存媒體中。該電腦程式被執行時，上述各方法實施例的步驟將可以被執行；而前述的儲存媒體包括：唯讀記憶體(Read Only Memory,ROM)、隨機存取記憶體(Random Access Memory,RAM)、磁碟、光碟、隨身碟等各種可以儲存程式碼的媒體。 A person of ordinary skill in the art can understand that a computer-readable storage medium is that all or part of the steps in the foregoing method embodiments can be implemented by hardware related to a computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the computer program is executed, the steps of the foregoing method embodiments can be executed; and the foregoing storage media includes: Read Only Memory (ROM) and Random Access Memory (RAM) , Floppy disks, compact discs, flash drives and other media that can store code.

雖然本發明所揭露之實施方式如上，惟所述之內容並非用以直接限定本發明之專利保護範圍。任何本發明所屬技術領域中具有通常知識者，在不脫離本發明所揭露之精神和範圍的前提下，對本發明之實施的形式上及細節上作些許之更動潤飾，均屬於本發明之專利保護範圍。本發明之專利保護範圍，仍須以所附之申請專利範圍所界定者為準。 Although the embodiments of the present invention are disclosed as above, the content described is not intended to directly limit the scope of patent protection of the present invention. Any person with ordinary knowledge in the technical field to which the present invention belongs, without departing from the spirit and scope of the present invention, makes slight modifications to the form and details of the implementation of the present invention, all belong to the patent protection of the present invention. Scope. The scope of patent protection of the present invention shall still be determined by the scope of the attached patent application.

步驟210 設定目標作物、種植地點、種植日期步驟220 取得種植地點之歷史氣候資料步驟230 依據歷史氣候資料推算目標作物之成熟日期步驟250 依據歷史氣候資料判斷目標作物由種植日期到成熟日期間之不利因素步驟260 依據不利因素判斷種植風險 Step 210 Set target crops, planting locations, and planting dates Step 220 Obtain historical climate data of the planting site Step 230 Calculate the maturity date of the target crop based on historical climate data Step 250 Determine the unfavorable factors of the target crop from the planting date to the mature date based on historical climate data Step 260 Judge the planting risk based on unfavorable factors

Claims

一種依據歷史氣候及種植歷史判斷種植風險之方法，應用於一電腦，該方法至少包含下列步驟：設定一目標作物、一種植地點、及一種植日期；由一伺服器取得該種植地點之一歷史氣候資料；由該伺服器或由預先儲存之記錄中讀出該種植地點之一歷史種植記錄；依據該歷史氣候資料及該歷史種植記錄中該目標作物之多個生長階段之生長天數與各該生長階段之至少一環境生態資料建立一生長模型，並將該種植日期輸入該生長模型使該生長模型推算該目標作物之一成熟日期；依據該歷史氣候資料中不同年度之該種植日期到該成熟日期間之氣候資料判斷該目標作物由該種植日期到該成熟日期間之各生長階段之風險分數，並計算該種植地點在該種植日期前一預定日期至該種植日期間之天氣資料與歷史氣候資料之相似度後，依據相似度對各該風險分數加權以調整該目標作物之各該生長階段之一風險分數；及依據各該風險分數之高低判斷一種植風險，該種植風險包含該目標作物之收成量差及/或收成品質不佳。 A method for judging planting risks based on historical climate and planting history, applied to a computer, the method includes at least the following steps: setting a target crop, a planting location, and a planting date; obtaining a history of the planting location from a server Climatic data; read out a historical planting record of the planting site from the server or from pre-stored records; according to the historical climate data and the growth days of the target crop in multiple growth stages in the historical planting record and each of the At least one environmental ecological data of the growth stage establishes a growth model, and inputs the planting date into the growth model to enable the growth model to calculate a maturity date of the target crop; according to the planting date to the maturity in different years in the historical climate data The climate data between the dates determine the risk scores of each growth stage of the target crop from the planting date to the maturity date, and calculate the weather data and historical climate of the planting site from a predetermined date before the planting date to the planting date After the similarity of the data, each risk score is weighted according to the similarity to adjust a risk score of each growth stage of the target crop; and a planting risk is judged based on the level of each risk score, and the planting risk includes the target crop The yield and/or quality of the harvest is poor.

如申請專利範圍第1項所述之依據歷史氣候及種植歷史判斷種植風險之方法，其中計算該種植地點在該種植日期前該預定日期至該種植日期間之天氣資料與歷史氣候資料之相似度之步驟為將當年氣候資料中氣溫、濕度及氣壓等項目整理成一多維度之向量表A，並將種植地點過去數年在同一時間的項目整理成相同維度的向量表B1、B2、B3、B4、…、Bn，及使用餘弦相似性(cosine similarity)計算該向量表A與各該向量表Bi(i=1~n)之相似度。 For example, the method for judging planting risks based on historical climate and planting history as described in item 1 of the scope of patent application, in which the similarity between weather data and historical climate data of the planting site from the scheduled date before the planting date to the planting date is calculated The step is to organize the temperature, humidity, and air pressure in the climate data of the current year into a multi-dimensional vector table A, and list the items of the planting site at the same time in the past few years Organize the items into vector tables B1, B2, B3, B4,..., Bn of the same dimension, and use cosine similarity to calculate the similarity between the vector table A and each of the vector tables Bi (i=1~n) .

如申請專利範圍第1項所述之依據歷史氣候及種植歷史判斷種植風險之方法，其中依據該歷史氣候資料中每一年由該種植日期到該成熟日期間之氣候資料判斷該目標作物由該種植日期到該成熟日期間之風險分數之步驟為依據該目標作物在不同生長階段之不利因素的影響時間與影響程度計算各生長階段之不利分數，並依據不利分數判斷風險分數。 For example, the method for judging planting risks based on historical climate and planting history as described in item 1 of the scope of patent application, in which the target crop is determined by the climatic data from the planting date to the maturity date in each year in the historical climatic data. The step of the risk score from the planting date to the maturity date is to calculate the disadvantage score of each growth stage according to the influence time and degree of the unfavorable factors in different growth stages of the target crop, and judge the risk score according to the disadvantage score.

如申請專利範圍第1項所述之依據歷史氣候及種植歷史判斷種植風險之方法，其中依據該歷史氣候資料中每一年由該種植日期到該成熟日期間之氣候資料判斷該目標作物由該種植日期到該成熟日期間之風險分數之步驟更包含依據預先設定之權重調整目標作物在不同生長階段之風險分數之步驟。 For example, the method for judging planting risks based on historical climate and planting history as described in item 1 of the scope of patent application, in which the target crop is determined by the climatic data from the planting date to the maturity date in each year in the historical climatic data. The step of risk scores from the planting date to the maturity date further includes the step of adjusting the risk scores of the target crop in different growth stages according to preset weights.

如申請專利範圍第1項所述之依據歷史氣候及種植歷史判斷種植風險之方法，其中由該伺服器或由預先儲存之記錄中讀出該種植地點之該歷史種植記錄之步驟，更包含對該歷史種植記錄中之種植資料所包含的資料計算以產生該種植資料中之部分項目之步驟。 For example, the method for judging planting risks based on historical climate and planting history as described in item 1 of the scope of patent application, wherein the step of reading the historical planting record of the planting site from the server or from the pre-stored records further includes The step of calculating the data contained in the planting data in the historical planting record to generate some items in the planting data.

一種依據歷史氣候及種植歷史判斷種植風險之系統，該系統至少包含：一目標設定模組，用以設定一目標作物、一種植地點、一種植日期；一資料取得模組，用以由一伺服器取得該種植地點之一歷史氣候資料，及用以由該伺服器或由預先儲存之記錄中讀出該種植地點之一歷史種植記錄；一生長推算模組，用以依據該歷史氣候資料及該歷史種植記錄中該目標作物之多個生長階段之生長天數與各該生長階段之至少一環境生態資料建立一生長模型，並將該種植日期輸入該生長模型使該生長模型推算該目標作物之一成熟日期；及一風險判斷模組，用以依據該歷史氣候資料中不同年度之該種植日期到該成熟日期間之氣候資料判斷該目標作物由該種植日期到該成熟日期間之各生長階段之風險分數，並計算該種植地點在該種植日期前一預定日期至該種植日期間之天氣資料與歷史氣候資料之相似度後，依據相似度對各該風險分數加權以計算該目標作物之各該生長階段之一風險分數，並依據各該風險分數之高低判斷一種植風險，該種植風險包含該目標作物之收成量差及/或收成品質不佳。 A system for judging planting risks based on historical climate and planting history. The system at least includes: a target setting module for setting a target crop, a planting location, and a planting date; The device obtains the historical climate data of the planting site, and is used to read out the historical planting record of the planting site from the server or from the pre-stored records; A growth calculation module for establishing a growth model based on the historical climate data and the growth days of the target crop in multiple growth stages in the historical planting record and at least one environmental and ecological data of each growth stage, and planting the Input the date into the growth model to enable the growth model to calculate a maturity date of the target crop; and a risk judgment module for judging the target based on the climate data from the planting date to the maturity date in different years in the historical climate data The risk score of each growth stage of the crop from the planting date to the maturity date is calculated, and the similarity between the weather data and historical climate data of the planting site from a predetermined date before the planting date to the planting date is calculated based on the similarity The degree weights each risk score to calculate a risk score for each growth stage of the target crop, and judges a planting risk based on the level of each risk score. The planting risk includes the yield difference and/or yield of the target crop Poor quality.

如申請專利範圍第6項所述之依據歷史氣候及種植歷史判斷種植風險之系統，其中該風險判斷模組是計算該種植地點在該種植日期前該預定日期至該種植日期間之天氣資料與歷史氣候資料之相似度之步驟為將當年氣候資料中氣溫、濕度及氣壓等項目整理成一多維度之向量表A，並將種植地點過去數年在同一時間的項目整理成相同維度的向量表B1、B2、B3、B4、…、Bn，及使用餘弦相似性(cosine similarity)計算該向量表A與各該向量表Bi(i=1~n)之相似度。 For example, the system for judging planting risks based on historical climate and planting history as described in item 6 of the scope of patent application, wherein the risk judgment module calculates the weather data and weather data of the planting site from the scheduled date before the planting date to the planting day The steps for the similarity of historical climate data are to organize the temperature, humidity, and air pressure in the climate data of the current year into a multi-dimensional vector table A, and organize the items of the planting site at the same time in the past few years into a vector table of the same dimension B1, B2, B3, B4,..., Bn, and use cosine similarity to calculate the similarity between the vector table A and each of the vector tables Bi (i=1~n).

如申請專利範圍第6項所述之依據歷史氣候及種植歷史判斷種植風險之系統，其中該風險判斷模組是依據該目標作物在不同生長階段之不利因素的影響時間與影響程度計算各生長階段之不利分數，並依據不利分數判斷風險分數。 For example, the system for judging planting risks based on historical climate and planting history as described in item 6 of the scope of patent application, wherein the risk judging module calculates each growth stage based on the influence time and degree of the unfavorable factors in different growth stages of the target crop The unfavorable score, and the risk score is judged based on the unfavorable score.

如申請專利範圍第6項所述之依據歷史氣候及種植歷史判斷種植風險之系統，其中該風險判斷模組更用以依據預先設定之權重調整目標作物在不同生長階段之風險分數。 For example, the system for judging planting risks based on historical climate and planting history as described in item 6 of the scope of patent application, wherein the risk judging module is further used to adjust the risk scores of target crops at different growth stages based on preset weights.

一種電腦可讀之儲存媒體，該儲存媒體儲存有一電腦程式，該電腦程式被處理器執行，以實現申請專利範圍第1項至第5項中任一項之依據歷史氣候及種植歷史判斷種植風險之方法。A computer-readable storage medium that stores a computer program that is executed by a processor to realize any one of items 1 to 5 of the scope of patent application for determining planting risks based on historical climate and planting history的方法。 The method.