TWI720842B - Intelligent bamboo shoot cultivation and harvesting monitoring system and method - Google Patents
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Abstract
一種智慧型竹筍栽培採收監控系統,執行一種智慧型竹筍栽培採收監控方法,包含:在一移動裝置上設置一攝影模組以取得一竹筍的一影像資料;該移動裝置的一影像擷取模組擷取該影像資料送至該移動裝置的一影像判斷模組;該影像判斷模組在該移動裝置上根據一影像深度學習模型即時判斷該影像資料的一判斷結果為待處理或無需處理,該影像判斷模組再由一生長時間與一預設週期時間判斷該判斷結果為待採收處理或待覆土處理。藉此,在該移動裝置上就可以直接得到該影像資料的判斷結果,無需每一次都要將該影像資料傳回該伺服器端才能進行判斷。 An intelligent bamboo shoot cultivation and harvesting monitoring system, which implements an intelligent bamboo shoot cultivation and harvesting monitoring method, includes: arranging a camera module on a mobile device to obtain an image data of a bamboo shoot; and capturing an image of the mobile device The module captures the image data and sends it to an image judging module of the mobile device; the image judging module on the mobile device real-time judges that a judgment result of the image data is to be processed or does not need to be processed according to an image deep learning model , The image judging module judges whether the judging result is to be harvested or to be covered with soil based on a long lifetime and a preset period of time. In this way, the judgment result of the image data can be directly obtained on the mobile device, and the image data does not need to be sent back to the server for judgment every time.
Description
本發明係關於一種竹筍栽培採收監控系統及方法,特別是指一種智慧型的竹筍栽培採收監控系統及方法。 The invention relates to a bamboo shoot cultivation and harvesting monitoring system and method, in particular to an intelligent bamboo shoot cultivation and harvesting monitoring system and method.
種植農作物時,農夫往往都需要頻繁前往巡視,但隨著農夫的高齡化、工資高漲導致的人事成本提升、青年從農意願低落等等勞動力不足的問題,使得農夫逐漸使用電子監控系統取代傳統的人力巡視。 When growing crops, farmers often need to visit frequently. However, with the aging of farmers, the increase in personnel costs caused by rising wages, and the low willingness of young people to farm, farmers are gradually using electronic monitoring systems to replace traditional labor costs. Manpower inspection.
例如有中華民國專利公告號I662505提供了一種菌類生長影像監控系統,適用於室內菌類培養場,室內菌類培養場至少包括培養架及承載於培養架上的菌類,菌類生長影像監控系統包括空拍機與管理主機。空拍機具有定位模組與影像擷取模組,定位模組具有多個超音波感測元件以進行定位,影像擷取模組用於拍攝菌類以產生影像資料。管理主機與空拍機互相通訊,並適於根據排程軌跡資料庫及這些超音波感測元件的定位資料以驅動空拍機沿預設路徑移動。 For example, the Republic of China Patent Announcement No. I662505 provides a fungus growth image monitoring system, which is suitable for indoor fungus cultivation fields. The indoor fungus cultivation field includes at least a culture rack and the fungi carried on the culture rack. The fungus growth image monitoring system includes an aerial camera. With the management host. The aerial camera has a positioning module and an image capturing module. The positioning module has a plurality of ultrasonic sensing elements for positioning. The image capturing module is used to shoot fungi to generate image data. The management host communicates with the aerial camera, and is adapted to drive the aerial camera to move along a preset path according to the scheduled trajectory database and the positioning data of these ultrasonic sensing components.
然而,前述專利案中,空拍機產生影像資料後,仍須將影像資料傳送至管理主機,才能在管理主機中產生判斷資料,無法在空拍機上就產生判斷資料。除此之外,使用者無法從判斷資料中直接得知應該採取什麼行動、是否可以採收,使用上仍有諸多不便。 However, in the aforementioned patent case, after the aerial camera generates the image data, the image data must be sent to the management host to generate the judgment data in the management host, and the judgment data cannot be generated on the aerial camera. In addition, the user cannot directly know from the judgment data what action should be taken and whether it can be harvested, and there are still many inconveniences in use.
爰此,本發明人提出一種智慧型竹筍栽培採收監控系統,用於拍攝一竹筍,該智慧型竹筍栽培採收監控系統包含:一伺服器端;一移動裝置,包含一單板電腦,該單板電腦訊號連接該伺服器端,該單板電腦有一影像擷取模組、一影像判斷模組及一GPS模組,該影像判斷模組為圖形處理器,該影像判斷模組儲存有一資料庫,該資料庫為一影像深度學習模型;以及一攝影模組,設置在該移動裝置上並訊號連接該影像擷取模組;當該攝影模組取得該竹筍的一影像資料後,該影像判斷模組在該移動裝置上根據該資料庫即時判斷該影像資料的一判斷結果為待處理或無需處理,並將該判斷結果回傳至該伺服器端;當該影像資料符合該資料庫的一冒頭影像時,該影像判斷模組判斷該判斷結果為待處理,當該影像資料符合該資料庫的一未冒頭影像時,該影像判斷模組判斷該判斷結果為無需處理;該冒頭影像為該竹筍有冒頭,該未冒頭影像為該竹筍沒有冒頭;當該影像資料符合該資料庫的該冒頭影像時,該影像判斷模組比較該GPS模組的一生長時間與一預設週期時間;當該生長時間與該預設週期時間相符合時,該影像判斷模組判斷該判斷結果為待採收處理;當該生長時間與該預設週期時間不符合時,該影像判斷模組判斷該判斷結果為待覆土處理。在本發明之實施方式中,該移動裝置為一自走車。 In this regard, the present inventor proposes an intelligent bamboo shoot cultivation and harvesting monitoring system for shooting a bamboo shoot. The intelligent bamboo shoot cultivation and harvesting monitoring system includes: a server end; a mobile device including a single board computer. The single-board computer signal is connected to the server. The single-board computer has an image capture module, an image judgment module, and a GPS module. The image judgment module is a graphics processor, and the image judgment module stores a data Database, the database is an image deep learning model; and a camera module, which is set on the mobile device and signally connected to the image capture module; when the camera module obtains an image data of the bamboo shoot, the image The judgment module on the mobile device judges in real time whether a judgment result of the image data is to be processed or does not need to be processed according to the database, and returns the judgment result to the server; when the image data conforms to the database In the case of an emerging image, the image determination module determines that the determination result is pending processing. When the image data matches an unemerged image in the database, the image determination module determines that the determination result does not require processing; the emerging image is The bamboo shoot has a head, and the non-head image means that the bamboo shoot has no head; when the image data matches the head image in the database, the image judgment module compares the lifetime of the GPS module with a preset cycle time; When the growth time matches the preset cycle time, the image determination module determines that the determination result is to be harvested; when the growth time does not match the preset cycle time, the image determination module determines the The judgment result is to be covered with soil. In the embodiment of the present invention, the mobile device is a self-propelled vehicle.
進一步,該伺服器端包含有一影像儲存模組。 Furthermore, the server side includes an image storage module.
進一步,有一使用者端訊號連接該伺服器端,該使用者端包含一影像觀看模組。 Furthermore, a user terminal signal is connected to the server terminal, and the user terminal includes an image viewing module.
進一步,該單板電腦包含有一電池檢測模組。 Further, the single board computer includes a battery detection module.
進一步,該攝影模組包含一紅外光攝影機及一可見光攝影機。 Further, the camera module includes an infrared light camera and a visible light camera.
本發明人再提出一種智慧型竹筍栽培採收監控方法,包含:在一移動裝置上設置一攝影模組以取得一竹筍的一影像資料;該移動裝置的一影像擷取模組擷取該影像資料送至該移動裝置的一影像判斷模組;該影像判斷模組在該移動裝置上根據一資料庫即時判斷該影像資料的一判斷結果為待處理或無需處理,當該影像資料符合該資料庫的一冒頭影像時,該影像判斷模組判斷該判斷結果為待處理,當該影像資料符合該資料庫的一未冒頭影像時,該影像判斷模組判斷該判斷結果為無需處理;該冒頭影像為該竹筍有冒頭,該未冒頭影像為該竹筍沒有冒頭;當該影像資料符合該資料庫的該冒頭影像時,該影像判斷模組比較一GPS模組的一生長時間與一預設週期時間;當該生長時間與該預設週期時間相符合時,該影像判斷模組判斷該判斷結果為待採收處理;當該生長時間與該預設週期時間不符合時,該影像判斷模組判斷該判斷結果為待覆土處理。 The inventor further proposes a smart bamboo shoot cultivation and harvesting monitoring method, which includes: arranging a camera module on a mobile device to obtain an image data of a bamboo shoot; an image capturing module of the mobile device capturing the image The data is sent to an image judgment module of the mobile device; the image judgment module real-timely judges a judgment result of the image data on the mobile device according to a database as to be processed or does not need to be processed, when the image data conforms to the data When an emerging image of the database is generated, the image determination module determines that the determination result is to be processed, and when the image data matches a non-emergent image of the database, the image determination module determines that the determination result does not need to be processed; The image shows that the bamboo shoot has a head, and the non-head image is that the bamboo shoot has no head; when the image data matches the head image of the database, the image judgment module compares the lifetime of a GPS module with a preset period Time; when the growth time matches the preset cycle time, the image judging module judges that the judgment result is to be harvested; when the growth time does not match the preset cycle time, the image judging module It is judged that the judgment result is to be covered with soil.
進一步,在該影像判斷模組即時判斷該判斷結果之前,一伺服器端依據該竹筍的複數照片建立該資料庫,該伺服器端並將該資料庫傳送至該影像判斷模組。 Further, before the image judgment module judges the judgment result in real time, a server side establishes the database based on the plural photos of the bamboo shoots, and the server side transmits the database to the image judgment module.
進一步,該資料庫為一影像深度學習模型,當該影像資料不符合該冒頭影像且不符合該未冒頭影像時,該影像判斷模組將該影像資料傳送至該伺服器端,該影像資料被標記後,該伺服器端再依據該影像資料更新該影像深度學習模型。 Further, the database is an image deep learning model. When the image data does not match the emergent image and does not meet the non-emergence image, the image determination module sends the image data to the server, and the image data is After marking, the server side updates the image deep learning model according to the image data.
根據上述技術特徵可達成以下功效: According to the above technical features, the following effects can be achieved:
1.在移動裝置上就可以直接得到影像資料的判斷結果,無需每一次都要將影像資料傳回伺服器端才能進行判斷,且藉由竹筍有無冒頭判斷是否 要處理,再藉由GPS模組的生長時間判斷要做什麼處理,大幅減少使用者的負擔。 1. The judgment result of the image data can be directly obtained on the mobile device, without the need to send the image data back to the server to make the judgment every time, and judge whether the bamboo shoots have emerged or not. It needs to be processed, and then the growth time of the GPS module is used to determine what processing to do, which greatly reduces the burden on the user.
2.紅外光攝影機與可見光攝影機共同拍攝影像資料,無需擔心夜晚或天氣不佳影響準確性。 2. The infrared light camera and the visible light camera take the image data together, so there is no need to worry about the accuracy of the night or bad weather.
3.電池檢測模組在電量低於設定閾值時就可以先提示使用者,避免移動裝置在使用過程中突然沒電,造成使用者的困擾。 3. The battery detection module can prompt the user first when the power is lower than the set threshold, so as to avoid the sudden loss of power during the use of the mobile device, which will cause the user to be troubled.
4.當在移動裝置上無法直接得到判斷結果時,影像判斷模組會將影像資料回傳至伺服器端,可以更新影像深度學習模型。 4. When the judgment result cannot be obtained directly on the mobile device, the image judgment module will return the image data to the server, and the image deep learning model can be updated.
1:自走車 1: Self-propelled car
11:攝影模組 11: Photography module
111:紅外光攝影機 111: Infrared camera
112:可見光攝影機 112: Visible light camera
12:超音波模組 12: Ultrasonic module
13:鋰電池 13: Lithium battery
14:單板電腦 14: Single board computer
141:影像擷取模組 141: Image capture module
142:影像判斷模組 142: Image Judgment Module
143:電池檢測模組 143: Battery detection module
144:GPS模組 144: GPS module
2:伺服器端 2: server side
21:網路模組 21: Network module
22:影像儲存模組 22: Image storage module
23:報告圖表模組 23: report chart module
24:訊息通報模組 24: Message notification module
3:使用者端 3: User side
31:影像觀看模組 31: Image viewing module
32:自走車控制模組 32: Self-propelled car control module
33:訊息接收模組 33: Message receiving module
[第一圖]係本發明實施例之實施示意圖。 [The first figure] is a schematic diagram of the implementation of the embodiment of the present invention.
[第二圖]係本發明實施例之系統方塊圖。 [The second figure] is a system block diagram of an embodiment of the present invention.
[第三圖]係本發明實施例之流程圖一。 [Third Figure] is the first flow chart of the embodiment of the present invention.
[第四圖]係本發明實施例之流程圖二。 [Fourth Figure] is the second flowchart of the embodiment of the present invention.
綜合上述技術特徵,本發明智慧型竹筍栽培採收監控系統及方法的主要功效將可於下述實施例清楚呈現。 Based on the above technical features, the main effects of the intelligent bamboo shoot cultivation and harvesting monitoring system and method of the present invention will be clearly presented in the following embodiments.
請參閱第一圖及第二圖,係揭示本發明智慧型竹筍栽培採收監控系統的實施例,包含:一自走車(1)、一伺服器端(2)及一使用者端(3)。 Please refer to the first and second figures, an embodiment of the intelligent bamboo shoot cultivation and harvesting monitoring system of the present invention is disclosed, including: a self-propelled vehicle (1), a server end (2), and a user end (3) ).
該自走車(1)上設置有一攝影模組(11)、一超音波模組(12)、一鋰電池(13)及一單板電腦(14)。該單板電腦(14)訊號連接該伺服器端(2),該單板電腦(14)具有5T的運算能力,該單板電腦(14)包含一影像擷取模組(141)、一影像判斷模組(142)、一電池檢測模組(143)及一GPS模組(144),該影像判斷模組(142) 為圖形處理器。該攝影模組(11)包含一紅外光攝影機(111)及一可見光攝影機(112),該攝影模組(11)訊號連接該影像擷取模組(141)。藉由該紅外光攝影機(111)及該可見光攝影機(112),無需擔心夜晚或天氣不佳會使得該影像資料的準確率降低,該紅外光攝影機(111)及該可見光攝影機(112)可以再藉由USB影片類別(USB Video Class,UVC)連接電腦,以將該紅外光攝影機(111)及該可見光攝影機(112)的影像傳輸至電腦。 The self-propelled vehicle (1) is provided with a photographing module (11), an ultrasonic module (12), a lithium battery (13) and a single board computer (14). The single-board computer (14) is connected to the server end (2) with signals. The single-board computer (14) has 5T computing power. The single-board computer (14) includes an image capture module (141) and an image The judgment module (142), a battery detection module (143) and a GPS module (144), the image judgment module (142) For the graphics processor. The photographing module (11) includes an infrared light camera (111) and a visible light camera (112), and the photographing module (11) is signally connected to the image capturing module (141). With the infrared light camera (111) and the visible light camera (112), there is no need to worry that night or bad weather will reduce the accuracy of the image data. The infrared light camera (111) and the visible light camera (112) can be used again. A USB Video Class (UVC) is connected to a computer to transmit the images of the infrared light camera (111) and the visible light camera (112) to the computer.
該伺服器端(2)以無線網路的方式訊號連接該單板電腦(14),該伺服器端(2)包含一網路模組(21)、一影像儲存模組(22)、一報告圖表模組(23)及一訊息通報模組(24)。 The server end (2) is connected to the single-board computer (14) via a wireless network signal. The server end (2) includes a network module (21), an image storage module (22), and a Report chart module (23) and a message notification module (24).
該使用者端(3)訊號連接該伺服器端(2),該使用者端(3)包含一影像觀看模組(31)、一自走車控制模組(32)及一訊息接收模組(33)。 The user end (3) is signaled to the server end (2), and the user end (3) includes an image viewing module (31), a self-propelled vehicle control module (32) and a message receiving module (33).
請參閱第三圖,並請搭配第二圖,該伺服器端(2)依據一竹筍的複數照片建立一資料庫,該資料庫在本發明之實施方式中為一影像深度學習模型。所述照片事先由人工拍攝,並逐一標記所述照片的一冒頭影像或一未冒頭影像。該冒頭影像為該竹筍有冒頭,該未冒頭影像為該竹筍沒有冒頭,更明確的說,是依據所述照片中有無該竹筍的筍尖以判斷為該冒頭影像或該未冒頭影像。該伺服器端(2)接著將該影像深度學習模型傳送至該影像判斷模組(142)。 Please refer to the third figure with the second figure. The server (2) creates a database based on the plural photos of a bamboo shoot. The database is an image deep learning model in the embodiment of the present invention. The photo is taken manually in advance, and an emerging image or a non-emerging image of the photo is marked one by one. The rising image is that the bamboo shoot has a rising head, and the non-rising image is that the bamboo shoot has no rising head. More specifically, it is judged as the rising image or the non-rising image based on whether there is a shoot tip of the bamboo shoot in the photo. The server (2) then sends the image deep learning model to the image judgment module (142).
請參閱第四圖,並請搭配第二圖,將該攝影模組(11)設置在該自走車(1)上後,該自走車(1)可以藉由該單板電腦(14)的該GPS模組(144)規劃路線、藉由該使用者端(3)的該自走車控制模組(32)操作路線、或是藉由該超音波模組(12)自動避開障礙物而移動。該自走車(1)移動後,該攝影模組(11)拍攝該竹筍的所在位置以取得該竹筍的一影像資料,該影像資料為影片格式,該竹筍的 所在位置可以事先儲存在該GPS模組(144)中。該影像擷取模組(141)藉由邊緣檢測擷取該影像資料,並將該影像資料送至該影像判斷模組(142),該影像判斷模組(142)在該自走車(1)上直接根據該影像深度學習模型即時判斷該影像資料的一判斷結果,無需每一次都要將該影像資料回傳至該伺服器端(2)才能取得該判斷結果。當該影像資料符合該影像深度學習模型的該冒頭影像時,該影像判斷模組(142)判斷該判斷結果為待處理,當該影像資料符合該影像深度學習模型的該未冒頭影像時,該影像判斷模組(142)判斷該判斷結果為無需處理。 Please refer to the fourth figure, and please match the second figure. After the camera module (11) is installed on the self-propelled vehicle (1), the self-propelled vehicle (1) can be operated by the single board computer (14) The GPS module (144) of the GPS module (144) plans the route, the self-propelled car control module (32) of the user terminal (3) operates the route, or the ultrasonic module (12) automatically avoids obstacles Move. After the self-propelled vehicle (1) moves, the photographing module (11) photographs the location of the bamboo shoot to obtain an image data of the bamboo shoot. The image data is in a video format. The location can be stored in the GPS module (144) in advance. The image capturing module (141) captures the image data by edge detection, and sends the image data to the image determining module (142), and the image determining module (142) is in the self-propelled vehicle (1). ) Directly based on the image deep learning model to determine a judgment result of the image data in real time, and it is not necessary to return the image data to the server (2) every time to obtain the judgment result. When the image data matches the emerging image of the image deep learning model, the image determination module (142) determines that the determination result is to be processed. When the image data matches the unemerged image of the image deep learning model, the image determination module (142) determines that the result of the determination is to be processed. The image judgment module (142) judges that the judgment result is no need to process.
當該影像資料符合該影像深度學習模型的該冒頭影像時,該影像判斷模組(142)比較該GPS模組(144)儲存的一生長時間與一預設週期時間,該生長時間為該竹筍實際生長的時間,可以藉由同樣儲存在該GPS模組(144)之該竹筍的種植時間與該攝影模組(11)取得該影像資料的時間計算得到該生長時間;當該生長時間與該預設週期時間相符合時,該影像判斷模組(142)判斷該判斷結果為待採收處理;當該生長時間與該預設週期時間不符合時,該影像判斷模組(142)判斷該判斷結果為待覆土處理。舉例來說,若該竹筍的該預設週期時間介於N-3天至N+3天之間,當該GPS模組(144)的該生長時間為N-1天時,符合該預設週期時間,該影像判斷模組(142)會判斷該判斷結果為待採收處理;而當該GPS模組(144)的該生長時間為N-5天時,不符合該預設週期時間,該影像判斷模組(142)則會判斷該判斷結果為待覆土處理。藉由該冒頭影像及該未冒頭影像判斷為待處理或無需處理,再藉由該生長時間判斷是待採收處理還是待覆土處理,可以快速判斷該竹筍的適當處理方式,大大減輕該使用者的負擔。 When the image data matches the emergence image of the image deep learning model, the image determination module (142) compares the lifetime long time stored by the GPS module (144) with a preset period time, and the growth time is the bamboo shoot The actual growth time can be calculated from the planting time of the bamboo shoots also stored in the GPS module (144) and the time when the photographing module (11) obtains the image data; when the growth time and the When the preset cycle time matches, the image judging module (142) judges that the judging result is to be harvested; when the growth time does not match the preset cycle time, the image judging module (142) judges the The judgment result is to be covered with soil. For example, if the preset cycle time of the bamboo shoots is between N-3 days and N+3 days, when the growth time of the GPS module (144) is N-1 days, it conforms to the preset Cycle time, the image judgment module (142) will judge the judgment result to be harvested; and when the growth time of the GPS module (144) is N-5 days, it does not meet the preset cycle time, The image judgment module (142) judges that the judgment result is to be covered with soil. By judging whether the emerging image and the unemerged image are to be processed or need not be processed, and then judging whether it is to be harvested or to be covered by the growth time, the appropriate processing method for the bamboo shoot can be quickly determined, which greatly reduces the user Burden.
除了該竹筍的該冒頭影像及該未冒頭影像辨別,該影像判斷模組(142)也可以根據土壤顏色判斷土壤的濕度。更詳細的說,可以先選擇一種肥料, 以該攝影模組(11)拍攝使用這種肥料下不同濕度的土壤,以在該伺服器端(2)建立土壤濕度的該影像深度學習模型,並傳送至該影像判斷模組(142),之後該影像判斷模組(142)便可以比對土壤濕度的該影像深度學習模型及該影像資料,得到該土壤的濕度。 In addition to the identification of the rising image and the non-rising image of the bamboo shoot, the image judging module (142) can also judge the soil moisture according to the soil color. In more detail, you can choose a fertilizer first, The photographing module (11) is used to photograph soils with different humidity under the fertilizer, so as to establish the image deep learning model of soil moisture on the server side (2), and send it to the image judgment module (142), Then the image judging module (142) can compare the image deep learning model of soil moisture with the image data to obtain the soil moisture.
該影像判斷模組(142)判斷該判斷結果之後,該影像判斷模組(142)會將該影像資料壓縮至H.264標準,經由虛擬私人網路(Virtual Private Network,VPN)傳送至該伺服器端(2),以將該影像資料儲存至該影像儲存模組(22),該伺服器端(2)並同樣經由VPN將該影像資料傳送至該使用者端(3)的該影像觀看模組(31)供一使用者觀看,藉由VPN,可以避免該影像資料的串流受到他人的干擾。同時,該影像判斷模組(142)將該判斷結果經由該伺服器端(2)的該訊息通報模組(24)傳送至該使用者端(3)的該訊息接收模組(33)供該使用者觀看。 After the image judgment module (142) judges the judgment result, the image judgment module (142) compresses the image data to the H.264 standard, and sends it to the server via a virtual private network (Virtual Private Network, VPN) The server end (2) to store the image data to the image storage module (22), the server end (2) and also send the image data to the user end (3) through the VPN for viewing the image The module (31) is for a user to watch. With the VPN, the streaming of the image data can be prevented from being interfered by others. At the same time, the image judgment module (142) sends the judgment result to the message receiving module (33) of the user end (3) via the message notification module (24) of the server side (2). The user watched.
當該影像資料不符合該冒頭影像且不符合該未冒頭影像時,該影像判斷模組(142)將該影像資料傳送至該伺服器端(2),並在該使用者標記該影像資料為該冒頭影像或該未冒頭影像後,該伺服器端(2)依據該影像資料更新該影像深度學習模型,並將該影像資料儲存至該影像儲存模組(22)。 When the image data does not match the emerging image and does not match the non-emerging image, the image determination module (142) sends the image data to the server (2), and marks the image data as After the emerging image or the non-emerging image, the server end (2) updates the image deep learning model according to the image data, and stores the image data in the image storage module (22).
當該使用者對該竹筍進行處理後,該影像資料會發生變化,例如該使用者出現在該影像資料中、該自走車(1)的位置改變或落葉位置變化等等,該影像判斷模組(142)藉此判斷該使用者是否有去對該竹筍進行處理。該伺服器端(2)的該報告圖表模組(23)再依據該影像資料建立該判斷結果與實際處理情形的一報告圖表,以對該影像深度學習模型進行修正。舉例來說,該判斷結果為 無需處理時,該使用者卻對該竹筍進行處理,即代表要對該影像深度學習模型進行修正,以提高該影像深度學習模型的準確率。 When the user processes the bamboo shoots, the image data will change. For example, the user appears in the image data, the position of the self-propelled vehicle (1) changes or the position of fallen leaves, etc., the image judgment model The group (142) judges whether the user has to process the bamboo shoots. The report chart module (23) of the server (2) then creates a report chart of the judgment result and the actual processing situation based on the image data to modify the image deep learning model. For example, the judgment result is When there is no need to process, the user processes the bamboo shoots, which means that the image deep learning model needs to be revised to improve the accuracy of the image deep learning model.
該自走車(1)的電力是依靠該鋰電池(13)供給,當該鋰電池(13)的電量低於一設定閾值後,該電池檢測模組(143)會經由該伺服器端(2)的該訊息通報模組(24),傳送一提示訊息給該使用者端(3)的該訊息接收模組(33),以通知該使用者需更換該鋰電池(13)或對該鋰電池(13)進行充電,避免該自走車(1)在使用過程中突然沒電,反而造成該使用者的困擾。 The power of the self-propelled vehicle (1) is supplied by the lithium battery (13). When the power of the lithium battery (13) is lower than a set threshold, the battery detection module (143) will pass through the server ( 2) The message notification module (24) sends a prompt message to the message receiving module (33) of the user terminal (3) to notify the user that the lithium battery (13) needs to be replaced or the The lithium battery (13) is charged to prevent the self-propelled vehicle (1) from suddenly running out of power during use, which may cause trouble to the user.
綜合上述實施例之說明,當可充分瞭解本發明之操作、使用及本發明產生之功效,惟以上所述實施例僅係為本發明之較佳實施例,當不能以此限定本發明實施之範圍,即依本發明申請專利範圍及發明說明內容所作簡單的等效變化與修飾,皆屬本發明涵蓋之範圍內。 Based on the description of the above embodiments, when one can fully understand the operation and use of the present invention and the effects of the present invention, but the above embodiments are only the preferred embodiments of the present invention, and the implementation of the present invention cannot be limited by this. The scope, that is, simple equivalent changes and modifications made according to the scope of the patent application of the present invention and the content of the description of the invention, are all within the scope of the present invention.
1:自走車 1: Self-propelled car
11:攝影模組 11: Photography module
111:紅外光攝影機 111: Infrared camera
112:可見光攝影機 112: Visible light camera
12:超音波模組 12: Ultrasonic module
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