TW202011222A - Data search method and data search system thereof - Google Patents

Data search method and data search system thereof Download PDF

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TW202011222A
TW202011222A TW108104107A TW108104107A TW202011222A TW 202011222 A TW202011222 A TW 202011222A TW 108104107 A TW108104107 A TW 108104107A TW 108104107 A TW108104107 A TW 108104107A TW 202011222 A TW202011222 A TW 202011222A
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TWI725375B (en
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詹詩涵
柯兆軒
藍國誠
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台達電子工業股份有限公司
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Abstract

The present disclosure relates to a data search method and a data search system thereof. The data search method includes the following steps of: receiving a first learning content data, wherein the first learning content data includes multiple first learning sections. Analysing the first learning content data by a may of Natural Language Processing to search multiple first keyword strings corresponding to each of the first learning content data. Then, receiving a search message. Analysing the search message by the may of Natural Language Processing to generate a search string. Comparing the search string with the first keyword strings. Generating a search list according to the first learning section, which corresponding to the first keyword string that matches the search string.

Description

資料搜尋方法及其資料搜尋系統Data search method and data search system

本揭示內容關於一種資料搜尋方法及其資料搜尋系統，特別是一種能根據搜尋訊息在資料庫中找到對應之學習資料的技術。This disclosure relates to a data search method and data search system, and in particular to a technology that can find corresponding learning materials in a database according to search information.

線上學習平台是指一種將眾多學習資料儲存於伺服器中，讓使用者能透過網際網路連線至伺服器而隨時瀏覽學習資料的網路服務。在現行的各類線上學習平台中，提供的學習資料類型包含影片、音訊、簡報、文件或論壇。An online learning platform refers to a network service that stores a large number of learning materials in a server, allowing users to connect to the server via the Internet and browse the learning materials at any time. In the current various online learning platforms, the types of learning materials provided include videos, audios, presentations, documents or forums.

由於線上學習平台中儲存的學習資料數量龐大，使用者需要根據自身需求輸入搜尋訊息，方能從線上學習平台中調閱到相關的學習資料。因此，線上學習平台的搜尋機制是否能精準辨識出使用者的搜尋訊息，且快速且正確地提供對應的學習資料給使用者，將是決定線上學習平台之服務效能的一大關鍵指標。Due to the large amount of learning materials stored in the online learning platform, users need to enter search information according to their own needs in order to retrieve relevant learning materials from the online learning platform. Therefore, whether the search mechanism of the online learning platform can accurately identify the user's search information and provide the corresponding learning data to the user quickly and correctly will be a key indicator for determining the service performance of the online learning platform.

本揭示內容之一態樣為一種資料搜尋方法。資料搜尋方法包含下列步驟：接收第一學習資料，其中第一學習資料包含複數個第一學習區段。對第一學習資料進行分析，以產生對應於每一第一學習區段的複數個第一關鍵字串。接收搜尋訊息。對搜尋訊息進行分析，以產生搜尋字串。比對搜尋字串及第一關鍵字串。根據與搜尋字串相符的該些第一關鍵字串所對應的該些第一學習區段，產生搜尋清單。One aspect of this disclosure is a data search method. The data search method includes the following steps: receiving first learning data, where the first learning data includes a plurality of first learning sections. The first learning data is analyzed to generate a plurality of first keyword strings corresponding to each first learning section. Receive search messages. Analyze the search information to generate a search string. Compare the search string with the first keyword string. A search list is generated according to the first learning sections corresponding to the first keyword strings that match the search string.

本揭示內容之另一態樣為一種資料搜尋系統。資料搜尋系統包含儲存單元、分析單元及運算單元。儲存單元用以儲存第一學習資料，其中第一學習資料包含複數個第一學習區段。分析單元用以根據第一學習資料產生對應於每一第一學習區段的複數個第一關鍵字串。分析單元還用以分析搜尋訊息，以產生搜尋字串。運算單元電性連接於分析單元。運算單元用以比對搜尋字串及該些第一關鍵字串，且根據與搜尋字串相符的該些第一關鍵字串所對應的該些第一學習區段，產生搜尋清單。Another aspect of this disclosure is a data search system. The data search system includes a storage unit, an analysis unit and an operation unit. The storage unit is used to store the first learning data, where the first learning data includes a plurality of first learning sections. The analyzing unit is used for generating a plurality of first keyword strings corresponding to each first learning section according to the first learning data. The analysis unit is also used to analyze the search information to generate a search string. The arithmetic unit is electrically connected to the analysis unit. The operation unit is used to compare the search word string with the first keyword strings, and generate a search list according to the first learning sections corresponding to the first keyword strings that match the search word string.

據此，由於資料搜尋系統能分別對第一學習資料的各第一學習區段進行分析，以在各第一學習區段上建立供索引的第一關鍵字串，因此，後續接收到搜尋訊息時，資料搜尋系統將能透過比對搜尋訊息與第一關鍵字串，而精確地搜尋出第一學習資料所對應的第一學習區段，讓使用者能快速地開始瀏覽想查找的學習內容，大幅提昇學習效率。According to this, since the data search system can separately analyze each first learning section of the first learning data to create a first keyword string for indexing on each first learning section, the search message is subsequently received At this time, the data search system will be able to accurately search for the first learning section corresponding to the first learning data by comparing the search information with the first keyword string, so that the user can quickly start browsing the learning content they want to find To greatly improve learning efficiency.

以下將以圖式揭露本案之複數個實施方式，為明確說明起見，許多實務上的細節將在以下敘述中一併說明。然而，應瞭解到，這些實務上的細節不應用以限制本案。也就是說，在本揭示內容部分實施方式中，這些實務上的細節是非必要的。此外，為簡化圖式起見，一些習知慣用的結構與元件在圖式中將以簡單示意的方式繪示之。In the following, a plurality of embodiments of the case will be disclosed in a diagram. For the sake of clarity, many practical details will be described together in the following description. However, it should be understood that these practical details should not be used to limit the case. That is to say, in some embodiments of the present disclosure, these practical details are unnecessary. In addition, in order to simplify the drawings, some conventional structures and elements will be shown in a simple schematic manner in the drawings.

於本文中，當一元件被稱為「連接」或「耦接」時，可指「電性連接」或「電性耦接」。「連接」或「耦接」亦可用以表示二或多個元件間相互搭配操作或互動。此外，雖然本文中使用「第一」、「第二」、…等用語描述不同元件，該用語僅是用以區別以相同技術用語描述的元件或操作。除非上下文清楚指明，否則該用語並非特別指稱或暗示次序或順位，亦非用以限定本發明。In this article, when an element is referred to as "connected" or "coupled", it can be referred to as "electrically connected" or "electrically coupled." "Connected" or "coupled" can also be used to indicate that two or more components interact or interact with each other. In addition, although terms such as "first", "second", etc. are used herein to describe different elements, the terms are only used to distinguish elements or operations described in the same technical terms. Unless the context clearly dictates, the term does not specifically refer to or imply order or order, nor is it intended to limit the present invention.

現有的線上學習平台中，當使用者輸入搜尋訊息時，伺服器僅會將搜尋訊息與學習資料的檔案名稱進行比對，以篩選出相似的學習資料。然而，若學習資料的內容龐大（例如：片長為兩小時的影片），則使用者仍然需要手動調整學習資料（如：調整播放時間至第45分鐘），方能找出與自身需求最相關的區段。此外，若搜尋訊息過於口語化，則現有的線上學習平台亦可能會因為無法辨識搜尋訊息，而搜尋出毫無關連的學習資料。亦即，現有線上學習平台的搜尋機制並根據需求進行精細檢索。本揭示內容提供之資料搜尋系統及其方法將能改善此一現象。In the existing online learning platform, when a user enters a search message, the server only compares the search message with the file name of the learning material to filter out similar learning materials. However, if the content of the learning materials is huge (for example, a video with a length of two hours), users still need to manually adjust the learning materials (such as adjusting the playback time to the 45th minute) to find the most relevant to their needs Section. In addition, if the search information is too colloquial, the existing online learning platform may also search out unrelated learning materials because it cannot recognize the search information. That is, the search mechanism of the existing online learning platform performs fine retrieval according to demand. The data search system and method provided in this disclosure will improve this phenomenon.

請參閱第1A及1B圖所示，本揭示內容係關於一種資料搜尋系統100。資料搜尋系統100包含第一伺服器110、第二伺服器120及儲存單元130。第一伺服器110電性連接於第二伺服器120，而在其他實施例中，第一伺服器110及第二伺服器120間能透過網路建立連線，以進行資料傳輸。儲存單元130為一種資料儲存裝置，例如：快閃記憶體、記憶卡、硬碟等。在部分實施例中，儲存單元130儲存於獨立的伺服器中。在其他部分實施例中，儲存單元130可設於第一伺服器110或第二伺服器120中。在其他實施例中，第一伺服器110與第二伺服器120亦可整合為單一個伺服器。Please refer to FIGS. 1A and 1B. This disclosure relates to a data search system 100. The data search system 100 includes a first server 110, a second server 120, and a storage unit 130. The first server 110 is electrically connected to the second server 120. In other embodiments, the first server 110 and the second server 120 can establish a connection through the network for data transmission. The storage unit 130 is a data storage device, such as a flash memory, a memory card, and a hard disk. In some embodiments, the storage unit 130 is stored in a separate server. In some other embodiments, the storage unit 130 may be disposed in the first server 110 or the second server 120. In other embodiments, the first server 110 and the second server 120 can also be integrated into a single server.

在本實施例中，資料搜尋系統100係用以提供線上學習服務，例如：使用者可透過終端裝置200連線至第一伺服器110，以瀏覽線上學習介面。當使用者欲瀏覽學習內容時，第一伺服器110能由儲存單元130中取得對應的檔案。第二伺服器120則用以執行分類、管理與統計等功能。然而，本揭示內容的應用方式並不以此為限，資料搜尋系統100亦可應用於影音串流平台或網路討論論壇等。In this embodiment, the data search system 100 is used to provide online learning services. For example, a user can connect to the first server 110 through the terminal device 200 to browse the online learning interface. When the user wants to browse the learning content, the first server 110 can obtain the corresponding file from the storage unit 130. The second server 120 is used to perform functions such as classification, management, and statistics. However, the application method of the present disclosure is not limited to this, and the data search system 100 can also be applied to video streaming platforms or online discussion forums.

第一伺服器110用以接收數個學習資料。在部分實施例中，第一伺服器110係透過網際網路，由終端裝置200接收學習資料。學習資料可為影片、聲音、簡報或討論串。為便於說明，在本實施例中以將多個學習資料細分為第一學習資料131a、第二學習資料131b及第三學習資料131c進行說明。然而，本揭示內容並不以此為限，學習資料的數量係可任意調整。The first server 110 is used to receive several learning materials. In some embodiments, the first server 110 receives the learning data from the terminal device 200 via the Internet. The learning materials can be videos, sounds, presentations or discussion threads. For ease of explanation, in this embodiment, a plurality of learning materials are subdivided into a first learning material 131a, a second learning material 131b, and a third learning material 131c. However, this disclosure is not limited to this, and the number of learning materials can be adjusted arbitrarily.

在部分實施例中，當第一伺服器110接收第一學習資料131a後，第一伺服器110將第一學習資料131a上傳至儲存單元130的課程資料庫131中，第一學習資料131a中包含複數個第一學習區段。該些第一學習區段係依照時間順序（例如：第一學習資料131a中預設的時間軸）相互銜接（或排列）。例如：若第一學習資料131a為片長30分鐘的影片檔案，則第一學習資料131a可包含兩個第一學習區段，每個學習區段分別對應於15分鐘的片長。In some embodiments, after the first server 110 receives the first learning material 131a, the first server 110 uploads the first learning material 131a to the course database 131 of the storage unit 130. The first learning material 131a includes Plural first learning sections. The first learning sections are connected (or arranged) to each other in chronological order (for example, the time axis preset in the first learning data 131a). For example, if the first learning material 131a is a 30-minute film file, the first learning material 131a may include two first learning sections, each of which corresponds to a 15-minute film length.

如第1B圖所示，第二伺服器120包含運算單元121、分析單元122及傳輸單元123。運算單元121電性連接於分析單元122及傳輸單元123。第二伺服器120係透過傳輸單元123與第一伺服器110與儲存單元130進行資料傳輸。第二伺服器120能根據第一伺服器110傳來的分析訊息，由儲存單元130取得第一學習資料131a，並進行分析處理，以根據第一學習資料131a產生對應於每一個第一學習區段的複數個第一關鍵字串（keyword string）。As shown in FIG. 1B, the second server 120 includes an arithmetic unit 121, an analysis unit 122 and a transmission unit 123. The operation unit 121 is electrically connected to the analysis unit 122 and the transmission unit 123. The second server 120 performs data transmission with the first server 110 and the storage unit 130 through the transmission unit 123. The second server 120 can obtain the first learning data 131a from the storage unit 130 according to the analysis message transmitted from the first server 110, and perform analysis processing to generate corresponding first learning areas according to the first learning data 131a Multiple first keyword strings of the segment.

舉例而言，第一學習資料131a為影片檔案，且包含字幕檔案。分析單元122能針對字幕檔案中的文字，以語意分析技術（Natural Language Processing）建立出語意相關字串（或稱推論字串）。以產生對應於每個第一學習區段的第一關鍵字串，例如：「投影器、影像、原理」、「高頻訊號、銳化、增強」及「提升、清晰度」。在部份實施例中，語意相關字串可為字幕檔案內的原文，亦可為推論出的字詞，舉例而言，若字幕檔案中包含「蘋果、記憶體、處理器」，則分析單元122可自動推論出「智慧型手機、iphone」。當第二伺服器120產生第一關鍵字串後，第二伺服器120還能將第一關鍵字串儲存於儲存單元130的分析資料庫132中。在其他部分實施例中，第二伺服器120還會在分析資料庫132中儲存對應於第一學習資料131a的第一辨識代碼，使第一關鍵字串能根據第一辨識代碼，對應到課程資料庫131中的第一學習資料131a。For example, the first learning material 131a is a video file, and includes a subtitle file. The analysis unit 122 can use semantic analysis technology (Natural Language Processing) to create semantically related character strings (or inferred character strings) for the text in the subtitle file. To generate a first keyword string corresponding to each first learning section, for example: "projector, image, principle", "high-frequency signal, sharpening, enhancement" and "enhancement, clarity". In some embodiments, the semantically related strings may be the original text in the subtitle file or inferred words. For example, if the subtitle file includes "apple, memory, processor", the analysis unit 122 can automatically infer "smartphone, iphone". After the second server 120 generates the first keyword string, the second server 120 can also store the first keyword string in the analysis database 132 of the storage unit 130. In some other embodiments, the second server 120 may also store the first identification code corresponding to the first learning data 131a in the analysis database 132, so that the first keyword string can correspond to the course according to the first identification code The first learning material 131a in the database 131.

在部分實施例中，第一學習資料131a還包含時間軸資料，每個第一學習區段係根據時間軸資料相銜接，以組成第一學習資料131a。第一伺服器110可根據時間軸資料，正確地將第一學習資料131a中的第一學習區段傳送至終端裝置200，讓使用者能直接從正確的時間點開始瀏覽第一學習資料131a的內容。In some embodiments, the first learning material 131a further includes time-axis data, and each first learning section is connected according to the time-axis data to form the first learning material 131a. The first server 110 can correctly transmit the first learning section in the first learning data 131a to the terminal device 200 according to the time axis data, so that the user can directly browse the first learning data 131a from the correct time point content.

在第一伺服器110由終端裝置200處接收搜尋訊息時，第一伺服器110將搜尋訊息轉傳至第二伺服器120。第二伺服器120透過分析單元122對搜尋訊息進行分析處理，以產生搜尋字串，例如：搜尋訊息為「投影器的原理」，第二伺服器120能先對搜尋訊息進行斷句，以透過分析、擷取或推論，產生搜尋字串「投影器、原理」。When the first server 110 receives the search message from the terminal device 200, the first server 110 forwards the search message to the second server 120. The second server 120 analyzes the search message through the analysis unit 122 to generate a search string, for example: the search message is "the principle of the projector", and the second server 120 can first segment the search message to analyze , Extract or infer, generate the search string "projector, principle".

分析單元122用以解析使用者提交的文字內容，並提取文字中的人、事、物、地點等訊息，讓開發者能夠了解使用者真正意圖並推估出欲詢問的問題答案。分析單元122可對搜尋訊息進行斷詞，並建立詞向量（例如：透過word2vec、sentece2vec等分析模型），以推測出相似字。此外，分析單元122可透過網際網路，連線至語義網(ontology)，以進行推論。The analysis unit 122 is used to analyze the text content submitted by the user and extract information such as people, things, things, and places in the text, so that the developer can understand the real intention of the user and estimate the answer to the question to be asked. The analysis unit 122 can perform word segmentation on the search information and create word vectors (for example: through analysis models such as word2vec and sente2vec) to infer similar words. In addition, the analysis unit 122 can be connected to the semantic web (ontology) through the Internet for inference.

在部分實施例中，分析單元122內包含自動編碼器122a（Auto-encoder）。第二伺服器120可接收複數個訓練資料，並將該些訓練資料輸入自動編碼器器122a中，以透過資料壓縮處理及維度轉換處理，建立語意分析網路(Semantic network)122b。語意分析網路122b用以對第一學習資料及搜尋訊息進行語意分析。自動編碼器122a可利用深度學習建立語意分析網路122b。舉例而言，訓練資料包含複數個原始的學習資料以及已被確認的多個關鍵字串，自動編碼器122a能將原始的學習資料經語意分析後，轉換為嵌入向量，並根據已被確認的該些關鍵字串，產生對應的加權參數，以建立語意分析網路122b。由於本領域人士可理解語意分析之原理，在此不另贅述。In some embodiments, the analysis unit 122 includes an auto-encoder 122a (Auto-encoder). The second server 120 may receive a plurality of training data, and input the training data into the autoencoder 122a to establish a semantic analysis network (Semantic network) 122b through data compression processing and dimension conversion processing. The semantic analysis network 122b is used for semantic analysis of the first learning data and search information. The autoencoder 122a can use deep learning to build a semantic analysis network 122b. For example, the training data includes a plurality of original learning data and a plurality of keyword strings that have been confirmed. The autoencoder 122a can convert the original learning data into an embedding vector after semantic analysis, and according to the confirmed The keyword strings generate corresponding weighting parameters to establish a semantic analysis network 122b. Since those skilled in the art can understand the principle of semantic analysis, they are not repeated here.

在語意分析單元122分析取得搜尋字串後，運算單元121用以比對搜尋字串及該些第一關鍵字串，並根據與搜尋字串相符的第一關鍵字串所對應的第一學習區段，產生搜尋清單。舉例而言，搜尋字串「投影器、原理」與第一關鍵字串「投影器、影像、原理」相似，因此，運算單元122會將根據對應的第一學習區段列於搜尋清單上，以供使用者參考。請參閱第1A圖所示，若「第一學習資料131a的其中一個第一學習區段對應的第一關鍵字串」及「第二學習資料131b的其中一個第二學習區段對應的第二關鍵字串」皆與搜尋字串相似，則運算單元121將在搜尋清單上同時列出這兩個學習區段，且使用者可操作終端裝置200，於第一伺服器110提供的線上學習介面上點擊對應的學習區段，第一伺服器110即會提供對應的學習區段給終端裝置200（例如：某部影片從第15分鐘的時間點開始播放）。After the semantic analysis unit 122 analyzes and obtains the search string, the operation unit 121 compares the search string with the first keyword strings, and based on the first learning corresponding to the first keyword string that matches the search string Section to generate a search list. For example, the search string "projector, principle" is similar to the first keyword string "projector, image, principle". Therefore, the arithmetic unit 122 will list the corresponding first learning section on the search list, For user reference. Please refer to FIG. 1A, if "the first keyword string corresponding to one of the first learning sections of the first learning data 131a" and "the second key corresponding to one of the second learning sections of the second learning data 131b" "Keyword string" is similar to the search string, the computing unit 121 will list the two learning sections on the search list at the same time, and the user can operate the terminal device 200 to provide the online learning interface on the first server 110 Click the corresponding learning section on the top, and the first server 110 will provide the corresponding learning section to the terminal device 200 (for example, a certain video starts to play from the 15th minute).

據此，由於資料搜尋系統100能分別對第一學習資料131a的各第一學習區段進行語意分析，以在各第一學習區段上建立供索引的第一關鍵字串，因此，後續接收到搜尋訊息時，資料搜尋系統100將能比對搜尋訊息與第一關鍵字串，而精確地搜尋出第一學習資料131a中所對應的第一學習區段，讓使用者能快速地開始瀏覽想查找的學習內容，大幅提昇學習效率。此外，資料搜尋系統100亦能將搜尋訊息與第一關鍵字串的分析結果存於推薦資料庫134中，以在特定時刻（如：使用者瀏覽完一部影片或提出問題時），根據搜尋出的第一學習資料131a產生推薦訊息，並傳遞給終端裝置200。在部分實施例中，運算單元121還用以計算搜尋字串及第一關鍵字串之間的多個第一相似度。第一相似度即為搜尋字串與每個第一關鍵字串之間的匹配度。舉例而言，若搜尋字串為「投影機、原理」，第一關鍵字串中出現了「投影機」，則第一相似度為50％，若「投影機、原理」皆出現，則第一相似度為100％。運算單元121能判斷每個第一相似度是否大於門檻值（如：60％）？並只將第一相似度大於門檻值的第一關鍵字串所對應的第一學習區段列在搜尋清單上。According to this, since the data search system 100 can perform semantic analysis on each first learning segment of the first learning data 131a to create a first keyword string for indexing on each first learning segment, therefore, subsequent receiving When it comes to searching for information, the data search system 100 will be able to compare the search information with the first keyword string, and accurately search for the corresponding first learning section in the first learning data 131a, so that the user can quickly start browsing The learning content you want to find greatly improves learning efficiency. In addition, the data search system 100 can also store the analysis results of the search information and the first keyword string in the recommendation database 134, so that at a specific time (such as when the user browses a video or asks a question), according to the search The first learning material 131a that is generated generates a recommendation message and is transmitted to the terminal device 200. In some embodiments, the arithmetic unit 121 is also used to calculate multiple first similarities between the search string and the first keyword string. The first similarity is the matching degree between the search string and each first keyword string. For example, if the search string is "projector, principle", and "projector" appears in the first keyword string, the first similarity is 50%, and if both "projector, principle" appears, the first A similarity is 100%. The arithmetic unit 121 can determine whether each first similarity is greater than a threshold (eg, 60%)? Only the first learning section corresponding to the first keyword string whose first similarity is greater than the threshold is listed on the search list.

在部分實施例中，資料搜尋系統100會將使用者的動作記錄下來作為「行為紀錄」。行為紀錄包含但不限於：影片觀看紀錄、影片標記紀錄、 製作筆記紀錄、 評分紀錄、 分享紀錄、 討論區紀錄、上傳/編輯(影片)紀錄、切換頁紀錄。第二伺服器120可參照使用者的行為記錄，來排序搜尋清單中的學習資料。In some embodiments, the data search system 100 records the user's actions as "behavior records." Behavior records include, but are not limited to: video viewing records, video mark records, note making records, rating records, sharing records, forum records, upload/edit (video) records, and page switching records. The second server 120 may refer to the user's behavior records to sort the learning data in the search list.

如第1A圖所示，在部分實施例中，儲存單元130內儲存有第一學習資料131a、第二學習資料131b及第三學習資料131c。第二學習資料131b包含複數個第二學習區段，且每個第二學習區段中包含各自的第二關鍵字串；同理，第三學習資料131c包含複數個第三學習區段，且每個第三學習區段中包含各自的第三關鍵字串。當使用者透過終端裝置200，針對第二學習資料中的其中一個第二學習區段，傳送操作訊息至資料搜尋系統100時，第一伺服器110能接收操作訊息，且將對應的第二關鍵字串儲存到儲存單元130內的行為資料庫133中，以將對應的第二關鍵字串設定為一筆行為資料。在其他部分實施例中，運算單元121能在接收到操作訊息後，將第二關鍵字串記錄為行為資料。如第1B圖所示，當使用者在多次發出不同的操作訊息後，行為資料庫133即會記錄多筆對應的行為資料133a～133c。As shown in FIG. 1A, in some embodiments, the storage unit 130 stores the first learning material 131a, the second learning material 131b, and the third learning material 131c. The second learning material 131b includes a plurality of second learning sections, and each second learning section includes a respective second keyword string; similarly, the third learning material 131c includes a plurality of third learning sections, and Each third learning section contains its own third keyword string. When the user sends an operation message to the data search system 100 through the terminal device 200 for one of the second learning sections in the second learning data, the first server 110 can receive the operation message and send the corresponding second key The character string is stored in the behavior database 133 in the storage unit 130 to set the corresponding second keyword string as a piece of behavior data. In some other embodiments, the operation unit 121 can record the second keyword string as behavior data after receiving the operation message. As shown in FIG. 1B, when the user sends out different operation messages multiple times, the behavior database 133 will record a plurality of corresponding behavior data 133a-133c.

在運算單元121篩選出大於門檻值的第一相似度後，運算單元121還能根據行為資料庫133中的行為資料133a～133c，與篩選出的第一關鍵字串（即，與搜尋字串相符）進行分析計算，計算行為資料133a～133c以及第一關鍵字串間的複數個第二相似度（如：比對文句的相似程度）。第二相似度係對應於行為資料及每一個第一關鍵字串。舉例而言，運算單元121先比對搜尋訊息「投影器」與多個第一關鍵字串，篩選出兩個第一關鍵字串「投影器、原理」及「投影器、清晰度」後，運算單元121判斷行為資料庫133中儲存有行為資料「清晰度」，代表使用者過去曾瀏覽過關於「清晰度」主題的學習資料，此時，運算單元121判斷行為資料「清晰度」與第一關鍵字串「投影器、清晰度」的第二相似度較高，因此，運算單元121在產生搜尋清單時，會把第一關鍵字串「投影器、清晰度」所對應的第一學習區段排列在第一關鍵字串「投影器、原理」所對應的第一學習區段之前。After the operation unit 121 filters out the first similarity greater than the threshold, the operation unit 121 can also select the first keyword string (ie, the search string) according to the behavior data 133a-133c in the behavior database 133 (Conformity) Perform analysis and calculation, and calculate a plurality of second similarities between the behavior data 133a to 133c and the first keyword string (eg, the similarity of the comparison sentences). The second similarity corresponds to the behavior data and each first keyword string. For example, the arithmetic unit 121 first compares the search information "projector" with a plurality of first keyword strings, and after filtering out the two first keyword strings "projector, principle" and "projector, clarity", The operation unit 121 judges that the behavior data "clearness" is stored in the behavior database 133, which represents that the user has browsed the learning data on the topic of "clarity" in the past. At this time, the operation unit 121 judges the behavior data "clarity" and The second similarity of a keyword string "projector, sharpness" is relatively high. Therefore, when generating the search list, the arithmetic unit 121 will take the first learning corresponding to the first keyword string "projector, sharpness" The sections are arranged before the first learning section corresponding to the first keyword string "projector, principle".

在其他部分實施例中，當第一伺服器110或第二伺服器120將第二關鍵字串記錄為行為資料133a～133c時，第一伺服器110或第二伺服器120還會根據第二關鍵字串被記錄至行為資料庫133中的次數，分別針對行為資料133a～133c記錄各自的權重值133d～133f。舉例而言，使用者曾瀏覽過第二學習資料131b的其中一個第二學習區段三次，因此，該第二學習區段對應的第二關鍵字串將被記錄三次，而其對應的行為資料的權重值將較大（如：+3）。運算單元121可根據權重值，來調整第二相似度。舉例而言，若運算單元121將兩個第一關鍵字串與行為資料庫133中不同的行為資料133a～133c比對後，得到的兩個第二相似度皆為「40％」，但其中一個第一關鍵字串對應的行為資料133a～133c的加權值133d～133f較高，則運算單元會將調整對應的第二相似度（例如：+10％），以將該第一關鍵字串對應的第一學習區段放在搜尋清單中另一個第一學習區段之前。據此，搜尋清單即可根據使用者過去瀏覽過的內容題材，進行更個人化的排序與推薦。In some other embodiments, when the first server 110 or the second server 120 records the second keyword string as behavior data 133a-133c, the first server 110 or the second server 120 may The number of times the keyword string is recorded in the behavior database 133, and the respective weight values 133d to 133f are recorded for the behavior data 133a to 133c, respectively. For example, the user has browsed one of the second learning sections of the second learning data 131b three times. Therefore, the second keyword string corresponding to the second learning section will be recorded three times, and the corresponding behavior data Will have a larger weight value (eg +3). The operation unit 121 may adjust the second similarity according to the weight value. For example, if the computing unit 121 compares the two first keyword strings with different behavior data 133a-133c in the behavior database 133, the two second similarities obtained are both "40%", but When the weighted values 133d to 133f of the behavior data 133a to 133c corresponding to a first keyword string are high, the arithmetic unit will adjust the corresponding second similarity (for example: +10%) to use the first keyword string The corresponding first learning section is placed before another first learning section in the search list. Based on this, the search list can be more personalized and recommended based on the content the user has browsed in the past.

前述之操作指令可為學習資料的觀看記錄、影片標記記錄（如：使用者標記影片「重要」）、筆記、評分、分享動作、留言等。在部分實施例中，操作訊息係使運作單元121將第二學習資料131b傳送至終端裝置200，以便瀏覽。在其他部分實施例中，操作訊息係使運作單元121在課程資料庫131中寫入註解資料。註解資料對應於第二學習資料131b，可為使用者的學習筆記、評論、評分、分享、發問、討論區或者註解。The foregoing operation instructions may be viewing records of learning materials, video tagging records (eg, users mark videos as "important"), notes, ratings, sharing actions, and comments. In some embodiments, the operation message causes the operation unit 121 to send the second learning material 131b to the terminal device 200 for browsing. In other partial embodiments, the operation message causes the operation unit 121 to write annotation data in the course database 131. The annotation data corresponds to the second learning material 131b, which may be a user's study notes, comments, ratings, shares, questions, discussion area, or notes.

在部分實施例中，分析單元122係根據第一學習資料131a中的元數據（metadata），辨識出第一學習區段。元數據是用於描述資料屬性（property）的資訊，可視為第一學習資料131a中的欄位資料，例如：一部影片檔案中的標題（caption）、關鍵字（keywords）、摘要（summary）、標籤（tags）、討論（discussion）、回應（reply）等。分析單元122可根據元數據辨識出第一學習區段，再加以分別進行語意分析。In some embodiments, the analysis unit 122 recognizes the first learning section based on the metadata in the first learning material 131a. Metadata is information used to describe the properties of data. It can be regarded as field data in the first learning material 131a, for example: caption, keywords, summary in a video file. , Tags, discussion, reply, etc. The analyzing unit 122 can identify the first learning section according to the metadata, and then perform semantic analysis separately.

在部分實施例中，運算單元121還能利用詞嵌入技術（word-embedding），先將第一學習資料131a中的元數據進行二位化編碼，再將第一學習資料131a儲存至儲存單元130中。In some embodiments, the arithmetic unit 121 can also use word-embedding to first encode the metadata in the first learning material 131a into two digits, and then store the first learning material 131a in the storage unit 130 in.

前述利用元數據辨識學習區段的方式，係根據第一學習資料131a中事先設定好的欄位，確認學習區段。在其他部分實施例中，亦可由運算單元121分析後，在第一學習資料131a上劃分出第一學習區段。例如：運算單元121可在第一學習資料131a中加入第一分段標記，以劃分出多個第一學習區段。The aforementioned method of identifying the learning section by using metadata is to confirm the learning section according to the pre-set fields in the first learning material 131a. In other partial embodiments, the first learning section may be divided on the first learning data 131a after being analyzed by the arithmetic unit 121. For example, the arithmetic unit 121 may add a first segment mark to the first learning material 131a to divide a plurality of first learning sections.

請參閱第2圖，為本揭示內容之部份實施例中，資料搜尋系統100的運作方式示意圖。資料搜尋系統100分別用以接收第一學習資料131a及搜尋訊息210。資料搜尋系統100會對第一學習資料131a依序進行分段處理P01、二位化編碼P02後，將其儲存至課程資料庫131中，以建立索引。接著，當資料搜尋系統100接收到搜尋訊息210後，先對搜尋訊息進行分析處理P03（如：語意分析或元數據分析），再將已建立索引的第一學習資料131a（包含分析出的第一關鍵字串）與分析後的搜尋訊息210進行比對處理P04，並根據行為資料庫133內的行為資料，產生搜尋清單300。Please refer to FIG. 2, which is a schematic diagram of the operation mode of the data search system 100 in some embodiments of the present disclosure. The data search system 100 is used to receive the first learning data 131a and the search message 210, respectively. The data search system 100 sequentially performs segmentation processing on the first learning data 131a P01 and the binary code P02, and then stores it in the course database 131 to create an index. Then, after the data search system 100 receives the search message 210, it first analyzes the search message P03 (such as semantic analysis or metadata analysis), and then indexes the first learning material 131a (including the analyzed first (A keyword string) is compared with the analyzed search message 210 to perform a processing P04, and according to the behavior data in the behavior database 133, a search list 300 is generated.

接下來說明分段標記的產生方式，請同時參閱第1A圖及第3A圖，第3A圖係根據本揭示內容之部分實施例所繪示的第一學習資料131a之文字檔案示意圖。在部分實施例中，第一學習資料131a包含文字檔案A1（例如：字幕）。第二伺服器120接收到該第一學習資料131a後，將對文字檔案A1進行分析處理，例如：以語意分析方法以產生複數個特徵句。該些特徵句之間具有先後關係。接著，判斷相鄰之特徵句之間的相似度，以產生第一分段標記。Next, the generation method of the segment mark will be described. Please refer to FIG. 1A and FIG. 3A at the same time. FIG. 3A is a schematic diagram of a text file of the first learning material 131a according to some embodiments of the present disclosure. In some embodiments, the first learning material 131a includes a text file A1 (for example, subtitles). After receiving the first learning material 131a, the second server 120 will analyze and process the text file A1, for example, a semantic analysis method to generate a plurality of characteristic sentences. There is a sequential relationship between these characteristic sentences. Next, the similarity between adjacent feature sentences is judged to generate the first segment label.

舉例而言，文字檔案A1經分析後，產生的特徵句包含「投影機根據影像訊號調整發光單元」、「發光單元投射出的光線會被反射為影像畫面」、「在另一種類型的投影機中」。前述第一句特徵句及第二句特徵句皆具有相同的字詞「影像、發光」，相似度較高，而第二句特徵句與第三句特徵句的相似度則較低。因此，當第二伺服器120判斷相鄰之特徵句相似度低於預定值時（如：完全沒有相同字詞，或者其中一句為轉折語句，例如「在其他實施例中…」），第二伺服器120即會產生第一分段標記。以將文字檔案A1區分為多個第一學習區段A11～A14。For example, after analyzing the text file A1, the generated characteristic sentences include "the projector adjusts the light unit according to the image signal", "the light projected by the light unit will be reflected into the image screen", "in another type of projector in". Both the first feature sentence and the second sentence feature sentence have the same word "image, glow", and the similarity is higher, while the similarity between the second sentence feature sentence and the third sentence feature sentence is lower. Therefore, when the second server 120 determines that the similarity of adjacent feature sentences is lower than a predetermined value (eg, there is no identical word at all, or one of the sentences is a turning sentence, such as "in other embodiments..."), the second The server 120 will generate the first segment mark. The text file A1 is divided into a plurality of first learning sections A11 to A14.

在前述實施例中，文字檔案A1可經由語意分析技術產生特徵句，且分析特徵句之間的相似度，但本揭示內容並不以此為限。在部份實施例中，第二伺服器120中的處理器亦可先將文字檔案A1進行二位化處理，再透過數據比對，判斷相似性，以建立特徵句或判斷特徵句之間的相似度。In the foregoing embodiment, the text file A1 can generate feature sentences through semantic analysis technology, and analyze the similarity between the feature sentences, but the disclosure is not limited thereto. In some embodiments, the processor in the second server 120 may first perform binarization processing on the text file A1, and then determine the similarity through data comparison to create a feature sentence or determine between feature sentences Similarity.

前述實施例中之文字檔案係指影片的字幕或簡報的文字內容，而若是文字檔案為「網路論壇的討論內容」，則仍能以相同原理進行分段。相似地，若第一學習資料131a包含聲音檔案，則第二伺服器120可先透過語音辨識，產生文字檔案A1後，再進行分析處理，以取得多個特徵句。The text file in the foregoing embodiment refers to the text content of the subtitles or presentations of the video, and if the text file is "discussion content of the online forum", it can still be segmented according to the same principle. Similarly, if the first learning data 131a includes a sound file, the second server 120 may first generate a text file A1 through speech recognition, and then perform analysis processing to obtain multiple characteristic sentences.

在其他部分實施例中，請參閱第3B圖所示，第一學習資料131b包含影像檔案B1。影像檔案B1又包含複數個影像畫面B01～B04。影像畫面B01～B04可為影片檔案中依照時序銜接的多個幀畫面。第二伺服器120用以判斷相鄰該些影像畫面B01～B04的相似度，以產生第一分段標記。舉例而言，影像畫面B01～B02係用以顯示投影機的結構圖，而影像畫面B03～B04用以顯示光線投影的路徑圖。影像畫面B02、B03間的相似度較低，所以第二伺服器120能在影像畫面B02、B03間加入第一分段標記，以形成多個第一學習區段B11、B12。In some other embodiments, please refer to FIG. 3B, the first learning material 131b includes the image file B1. The image file B1 further includes a plurality of image frames B01-B04. The image frames B01-B04 can be multiple frame frames connected in time sequence in the video file. The second server 120 is used to determine the similarity between the adjacent image frames B01-B04 to generate a first segment mark. For example, the image frames B01-B02 are used to display the structure diagram of the projector, and the image frames B03-B04 are used to display the path map of the light projection. The similarity between the video frames B02 and B03 is low, so the second server 120 can add a first segment mark between the video frames B02 and B03 to form a plurality of first learning sections B11 and B12.

請再次參閱第3A圖所示，在此說明分析單元122分析出第一關鍵字串的方法如後。分析單元122對第一學習資料131a中的文字檔案A1進行分析處理（如：語意分析），以取得多個特徵字。接著，在第一學習資料131a被劃分為多個第一學習區段A11～A14或B11～B12後，第二伺服器120將會判斷特徵字在每一個第一學習區段A11～A14或B11～B12中的數量，當數量大於預設值時，即將其設定為第一關鍵字串。舉例而言，在文字檔案A1的一個第一學習區段A11中包含下列內容：「投影機根據影像訊號調整發光單元，發光單元投射出的光線會被反射為影像畫面」。分析單元122會先對前述文字檔案進行斷句，以篩選出多個字詞（如：投影機、根據、影像訊號、調整…等）。其中，「影像」出現2次、「發光單元」出現2次、「投影機、光線」皆分別出現1次。分析單元122可將出現2次的特徵字「影像、發光單元」設定為第一關鍵字串。Please refer to FIG. 3A again. Here, the method of analyzing the first keyword string by the analyzing unit 122 is as follows. The analysis unit 122 performs analysis processing (eg, semantic analysis) on the text file A1 in the first learning material 131a to obtain a plurality of feature words. Next, after the first learning material 131a is divided into a plurality of first learning sections A11-A14 or B11-B12, the second server 120 will determine that the characteristic word is in each first learning section A11-A14 or B11 ~ The number in B12, when the number is greater than the preset value, it is set as the first keyword string. For example, a first learning section A11 of the text file A1 includes the following content: "The projector adjusts the light-emitting unit according to the image signal, and the light projected by the light-emitting unit will be reflected into the image screen." The analysis unit 122 will first segment the aforementioned text file to filter out multiple words (such as: projector, basis, image signal, adjustment, etc.). Among them, "Image" appears twice, "Lighting Unit" appears twice, and "Projector, Light" appears once. The analyzing unit 122 may set the feature word "image, light emitting unit" appearing twice as the first keyword string.

同理，分析單元122在接收到搜尋訊息後，亦能先對搜尋訊息內的文字進行斷句，以取得搜尋字串。或者分析單元122能根據斷句後產生的多個字詞，將出現次數大於預設值的字詞設定為搜尋字串。Similarly, after receiving the search message, the analyzing unit 122 can also segment the text in the search message to obtain the search string. Or, the analysis unit 122 can set the words whose occurrence frequency is greater than the preset value as the search string according to the multiple words generated after sentence segmentation.

請參閱第4圖所示，係根據本揭示內容之部分實施例繪示的資料搜尋方法示意圖。資料搜尋方法包含下列步驟S401～S409。在步驟S401中，第一伺服器110接收第一學習資料131a，並將第一學習資料131a儲存至課程資料庫131中。第一學習資料131a包含複數個第一學習區段。在步驟S402中，第二伺服器120連線至儲存單元130，且透過分析單元122，對第一學習資料131a進行分析處理，以產生對應於每一個第一學習區段的第一關鍵字串。在部份實施例中，分析單元122係透過語意分析技術，搜尋出第一關鍵字串。在其他部份實施例中，分析單元亦可對第一學習資料131a進行二位化處理，以根據元數據比對及篩選出第一關鍵字串。Please refer to FIG. 4, which is a schematic diagram of a data search method according to some embodiments of the present disclosure. The data search method includes the following steps S401-S409. In step S401, the first server 110 receives the first learning material 131a and stores the first learning material 131a in the course database 131. The first learning material 131a includes a plurality of first learning sections. In step S402, the second server 120 is connected to the storage unit 130, and the analysis unit 122 analyzes the first learning data 131a to generate a first keyword string corresponding to each first learning section . In some embodiments, the analysis unit 122 searches for the first keyword string through semantic analysis technology. In some other embodiments, the analysis unit may also perform binarization processing on the first learning material 131a to compare and filter out the first keyword string according to the metadata.

在步驟S403中，第一伺服器110接收搜尋訊息，並交由第二伺服器120進行後端運算分析。在步驟S404中，分析單元122對搜尋訊息進行語意分析，以搜尋出搜尋字串。在步驟S405中，運算單元121比對搜尋字串及該些第一關鍵字串，以產生第一相似度。In step S403, the first server 110 receives the search message and passes it to the second server 120 for back-end calculation analysis. In step S404, the analysis unit 122 performs semantic analysis on the search message to search out the search string. In step S405, the arithmetic unit 121 compares the search word string with the first keyword strings to generate a first similarity.

在步驟S406中，當運算單元121判斷第一相似度大於門檻值時，根據該些第一關鍵字串所對應的該些第一學習區段，產生搜尋清單。在步驟S407中，比對行為資料庫133中行為資料及在步驟S406中篩選出的該些第一關鍵字串，以產生複數個第二相似度。在步驟S408中，根據第二相似度，排序搜尋清單內的第一學習區段。In step S406, when the computing unit 121 determines that the first similarity is greater than the threshold, a search list is generated according to the first learning sections corresponding to the first keyword strings. In step S407, the behavior data in the behavior database 133 and the first keyword strings selected in step S406 are compared to generate a plurality of second similarities. In step S408, the first learning section in the search list is ranked according to the second similarity.

前述步驟係以第一資料庫131a進行說明，但在其他實施例中，資料搜尋系統100係儲存有多筆學習資料131a～131c。分析單元122可對每個學習資料131a～131c進行語意分析，以搜尋出對應的關鍵字串。運算單元121則可將搜尋字串與每個學習資料131a～131c中的每個關鍵字串相比對，以找出對應於搜尋字串的學習區段。例如：第一學習資料131a的其中一個第一學習區段與搜尋字串最相近、第二學習資料131b的其中一個第二學習區段亦與搜尋字串有相關，且運算單元121能將這兩個學習區段皆顯示於搜尋清單中。The foregoing steps are described with the first database 131a, but in other embodiments, the data search system 100 stores a plurality of learning materials 131a-131c. The analysis unit 122 may perform semantic analysis on each of the learning materials 131a-131c to search out the corresponding keyword string. The arithmetic unit 121 may compare the search word string with each keyword string in each learning data 131a-131c to find a learning section corresponding to the search word string. For example: one of the first learning sections of the first learning material 131a is the closest to the search string, and one of the second learning sections of the second learning material 131b is also related to the search string, and the arithmetic unit 121 can use this Both learning sections are displayed in the search list.

雖然本揭示內容已以實施方式揭露如上，然其並非用以限定本發明內容，任何熟習此技藝者，在不脫離本發明內容之精神和範圍內，當可作各種更動與潤飾，因此本發明內容之保護範圍當視後附之申請專利範圍所界定者為準。Although the present disclosure has been disclosed as above by way of implementation, it is not intended to limit the content of the present invention. Anyone who is familiar with this skill can make various changes and modifications within the spirit and scope of the present content, so the present invention The protection scope of the content shall be deemed as defined by the scope of the attached patent application.

100:資料搜尋系統110:第一伺服器120:第二伺服器121:運算單元122:分析單元122a:自動編碼器122b:語意分析網路123:傳輸單元130:儲存單元131:課程資料庫131a:第一學習資料131b:第二學習資料131c:第三學習資料132:分析資料庫133:行為資料庫133a:行為資料133b:行為資料133c:行為資料133d:權重值133e:權重值133f:權重值200:終端裝置A1:文字檔案A11:學習區段A12:學習區段A13:學習區段A14:學習區段A21:學習區段A22:學習區段B1:影像檔案B01:影像畫面B02:影像畫面B03:影像畫面B04:影像畫面B11:學習區段B12:學習區段S401～S408:步驟100: data search system 110: first server 120: second server 121: arithmetic unit 122: analysis unit 122a: automatic encoder 122b: semantic analysis network 123: transmission unit 130: storage unit 131: course database 131a : First learning material 131b: Second learning material 131c: Third learning material 132: Analysis database 133: Behavior database 133a: Behavior data 133b: Behavior data 133c: Behavior data 133d: Weight value 133e: Weight value 133f: Weight Value 200: terminal device A1: text file A11: learning section A12: learning section A13: learning section A14: learning section A21: learning section A22: learning section B1: video file B01: video screen B02: video Screen B03: Video screen B04: Video screen B11: Learning section B12: Learning section S401 to S408: Steps

第1A圖為根據本揭示內容之部分實施例所繪示的資料搜尋系統的示意圖。 第1B圖為根據本揭示內容之部分實施例所繪示的第一伺服器及行為資料庫示意圖。 第2圖為根據本揭示內容之部分實施例所繪示的資料搜尋系統的運作方式示意圖。 第3A圖為根據本揭示內容之部分實施例所繪示的第一學習資料之文字檔案示意圖。 第3B圖為根據本揭示內容之部分實施例所繪示的第一學習資料之影像畫面示意圖。 第4圖為根據本揭示內容之部分實施例所繪示的資料搜尋方法的示意圖。FIG. 1A is a schematic diagram of a data search system according to some embodiments of the present disclosure. FIG. 1B is a schematic diagram of a first server and a behavior database according to some embodiments of the present disclosure. FIG. 2 is a schematic diagram of the operation mode of the data search system according to some embodiments of the present disclosure. FIG. 3A is a schematic diagram of a text file of first learning data according to some embodiments of the present disclosure. FIG. 3B is a schematic diagram of an image frame of first learning data according to some embodiments of the present disclosure. FIG. 4 is a schematic diagram of a data search method according to some embodiments of the present disclosure.

120:第二伺服器 120: second server

121:運算單元 121: arithmetic unit

122:分析單元 122: Analysis unit

122a:自動編碼器 122a: automatic encoder

122b:語意分析網路 122b: Semantic Analysis Network

123:傳輸單元 123: Transmission unit

133:行為資料庫 133: Behavior database

133a:行為資料 133a: Behavioral data

133b:行為資料 133b: Behavioral data

133c:行為資料 133c: Behavioral data

133d:權重值 133d: weight value

133e:權重值 133e: weight value

133f:權重值 133f: weight value

Claims (20)

一種資料搜尋方法，包含： 接收一第一學習資料，其中該第一學習資料包含複數個第一學習區段； 對該第一學習資料進行分析，以產生對應於每一第一學習區段的複數個第一關鍵字串； 接收一搜尋訊息； 對該搜尋訊息進行分析，以產生一搜尋字串； 比對該搜尋字串及該些第一關鍵字串；以及 根據與該搜尋字串相符的該些第一關鍵字串所對應的該些第一學習區段，產生一搜尋清單。A data search method includes: receiving a first learning material, wherein the first learning material includes a plurality of first learning sections; analyzing the first learning material to generate a corresponding to each first learning section A plurality of first keyword strings; receiving a search message; analyzing the search message to generate a search string; comparing the search string with the first keyword strings; and according to matching with the search string The first learning sections corresponding to the first keyword strings generate a search list. 如請求項1所述之資料搜尋方法，還包含： 計算該搜尋字串及該些第一關鍵字串間的複數個第一相似度，其中該些第一相似度分別對應於該搜尋字串及該些第一關鍵字串；以及 根據該些第一相似度大於一門檻值的該些第一關鍵字串所對應的該些第一學習區段，產生該搜尋清單。The data search method according to claim 1, further comprising: calculating a plurality of first similarities between the search string and the first keyword strings, wherein the first similarities correspond to the search string and The first keyword strings; and generating the search list according to the first learning sections corresponding to the first keyword strings whose first similarity is greater than a threshold. 如請求項1所述之資料搜尋方法，還包含： 接收一操作訊息，其中該操作訊息對應於一第二學習資料中的一第二學習區段，且該第二學習區段包含一第二關鍵字串；以及 將該操作訊息所對應的該第二關鍵字串儲存至一行為資料庫中，以記錄為一行為資料。The data search method according to claim 1, further comprising: receiving an operation message, wherein the operation message corresponds to a second learning section in a second learning data, and the second learning section includes a second A keyword string; and storing the second keyword string corresponding to the operation message in a behavior database to record it as behavior data. 如請求項3所述之資料搜尋方法，還包含： 計算該行為資料以及與該搜尋字串相符的該些第一關鍵字串間的複數個第二相似度；以及 根據該些第二相似度，排序該搜尋清單中的該些第一學習區段。The data search method according to claim 3, further comprising: calculating a plurality of second similarities between the behavior data and the first keyword strings matching the search string; and according to the second similarities, Sort the first learning sections in the search list. 如請求項4所述之資料搜尋方法，還包含： 根據該第二關鍵字串被儲存至該行為資料庫中的次數，設定該行為資料的一權重值；以及 根據該權重值，調整該些第二相似度。The data search method according to claim 4, further comprising: setting a weight value of the behavior data according to the number of times the second keyword string is stored in the behavior database; and adjusting the weights according to the weight value Second similarity. 如請求項3所述之資料搜尋方法，其中該操作訊息係用以將該第二學習資料傳送至一終端裝置。The data search method according to claim 3, wherein the operation message is used to transmit the second learning data to a terminal device. 如請求項3所述之資料搜尋方法，其中該操作訊息係用以在一課程資料庫中，寫入一註解資料，該註解資料對應於該第二學習資料。The data search method according to claim 3, wherein the operation message is used to write an annotation data in a course database, the annotation data corresponding to the second learning data. 如請求項1所述之資料搜尋方法，還包含： 接收複數個訓練資料； 將該些訓練資料輸入一自動編碼器，並透過資料壓縮處理及維度轉換處理，建立一語意分析網路；以及 利用該語意分析網路，對該第一學習資料及該搜尋訊息進行語意分析。The data search method described in claim 1 further includes: receiving a plurality of training data; inputting the training data into an automatic encoder, and establishing a semantic analysis network through data compression processing and dimension conversion processing; and using The semantic analysis network performs semantic analysis on the first learning data and the search information. 如請求項1所述之資料搜尋方法，其中在接收該第一學習資料後，根據該第一學習資料中的元數據，辨識出該些第一學習區段。The data search method according to claim 1, wherein after receiving the first learning material, the first learning sections are identified based on the metadata in the first learning material. 如請求項9所述之資料搜尋方法，還包含： 利用詞嵌入技術，將該第一學習資料中的元數據進行二位化編碼。The data search method as described in claim 9, further comprising: using word embedding technology to perform binary encoding on the metadata in the first learning material. 一種資料搜尋系統，包含： 一儲存單元，用以儲存一第一學習資料，其中該第一學習資料包含複數個第一學習區段； 一分析單元，用以根據該第一學習資料產生對應於每一第一學習區段的複數個第一關鍵字串；該分析單元還用以根據一搜尋訊息產生一搜尋字串；以及 一運算單元，電性連接於該分析單元，其中該運算單元用以比對該搜尋字串及該些第一關鍵字串，且根據與該搜尋字串相符的該些第一關鍵字串所對應的該些第一學習區段，產生一搜尋清單。A data search system includes: a storage unit for storing a first learning data, wherein the first learning data includes a plurality of first learning sections; and an analysis unit for generating a correspondence corresponding to the first learning data A plurality of first keyword strings in each first learning section; the analysis unit is also used to generate a search string based on a search message; and an arithmetic unit electrically connected to the analysis unit, wherein the arithmetic unit uses A search list is generated by comparing the search string and the first keyword strings, and according to the first learning sections corresponding to the first keyword strings that match the search string. 如請求項11所述之資料搜尋系統，其中該運算單元用以計算該搜尋字串及該些第一關鍵字串間的複數個第一相似度，該些第一相似度分別對應於該搜尋字串及該些第一關鍵字串；該運算單元用以根據該些第一相似度大於一門檻值的該些第一關鍵字串所對應的該些第一學習區段，產生該搜尋清單。The data search system according to claim 11, wherein the computing unit is used to calculate a plurality of first similarities between the search string and the first keyword strings, the first similarities corresponding to the search words, respectively String and the first keyword strings; the computing unit is used to generate the search list according to the first learning sections corresponding to the first keyword strings whose first similarity is greater than a threshold. 如請求項11所述之資料搜尋系統，其中該儲存單元還儲存有一第二學習資料，該第二學習資料包含一第二學習區段，且該第二學習區段包含一第二關鍵字串； 其中在該運算單元接收到一操作訊息後，該運算單元根據該操作訊息對應的該第二學習區段，將對應的該第二關鍵字串儲存至該儲存單元的一行為資料庫中，以記錄為一行為資料。The data search system according to claim 11, wherein the storage unit further stores a second learning data, the second learning data includes a second learning section, and the second learning section includes a second keyword string ; After the operation unit receives an operation message, the operation unit stores the corresponding second keyword string into a behavior database of the storage unit according to the second learning section corresponding to the operation message, Take records as behavioral data. 如請求項13所述之資料搜尋系統，其中該運算單元用以計算該行為資料以及與該搜尋字串相符的該些第一關鍵字串間的複數個第二相似度，該些第二相似度分別對應於該行為資料以及與該搜尋字串相符的該些第一關鍵字串；該運算單元還用以根據該些第二相似度，排序該搜尋清單中的該些第一學習區段。The data search system according to claim 13, wherein the operation unit is used to calculate the behavior data and the plurality of second similarities between the first keyword strings matching the search string, and the second similarities Corresponding to the behavior data and the first keyword strings corresponding to the search string, respectively, the arithmetic unit is also used to sort the first learning sections in the search list according to the second similarities. 如請求項14所述之資料搜尋系統，其中該行為資料庫內還儲存有一權重值，該權重值係該第二關鍵字串被儲存至該行為資料庫中的次數；該運算單元係用以根據該權重值，調整該些第二相似度。The data search system according to claim 14, wherein the behavior database further stores a weight value, the weight value is the number of times the second keyword string is stored in the behavior database; the operation unit is used to According to the weight value, the second similarities are adjusted. 如請求項13所述之資料搜尋系統，其中該操作訊息係用以將該第二學習資料傳送至一終端裝置。The data search system according to claim 13, wherein the operation message is used to transmit the second learning data to a terminal device. 如請求項13所述之資料搜尋系統，其中，該操作訊息係用以在該儲存單元的一課程資料庫中，寫入一註解資料，該註解資料對應於該第二學習資料。The data search system according to claim 13, wherein the operation message is used to write an annotation data in a course database of the storage unit, the annotation data corresponding to the second learning data. 如請求項11所述之資料搜尋系統，其中該分析單元還包含一自動編碼器，該語意單元用以將複數個訓練資料輸入至該自動編碼器，以透過資料壓縮處理及維度轉換處理，建立一語意分析網路。The data search system according to claim 11, wherein the analysis unit further includes an automatic encoder, and the semantic unit is used to input a plurality of training data to the automatic encoder to create a data compression process and a dimension conversion process Semantic analysis of the Internet. 如請求項11所述之資料搜尋系統，其中該分析單元係根據該第一學習資料中的元數據，辨識出該些第一學習區段。The data search system according to claim 11, wherein the analysis unit identifies the first learning sections based on the metadata in the first learning data. 如請求項19所述之資料搜尋系統，其中該運算單元係利用詞嵌入技術，將該第一學習資料的元數據進行二位化編碼，再將該第一學習資料儲存至該儲存單元中。The data search system according to claim 19, wherein the arithmetic unit uses word embedding technology to perform binary encoding on the metadata of the first learning material, and then stores the first learning material in the storage unit.
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