TWI798973B - Electronic device and method for estimating poor customer experience of wireless network - Google Patents
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本發明是有關於一種預測無線網路的用戶體驗劣化的電子裝置和方法。The present invention relates to an electronic device and method for predicting user experience degradation of a wireless network.
供應無線網路服務的業者通常是利用基地台的性能指標來分析和預測基地台的故障機率和故障原因。然而,在一些情況下,基地台的性能指標並無法真實反應客戶的用戶體驗。就算在基地台的性能指標良好的情況下,還是有可能發生用戶體驗劣化的事件。Providers of wireless network services usually use the performance indicators of base stations to analyze and predict the failure probability and cause of failure of base stations. However, in some cases, the performance index of the base station cannot truly reflect the user experience of the customer. Even when the performance index of the base station is good, events of user experience degradation may still occur.
本發明提供一種預測無線網路的用戶體驗劣化的電子裝置和方法,可基於用戶的回饋執行用戶體驗劣化的預測。The present invention provides an electronic device and method for predicting user experience degradation of a wireless network, which can perform prediction of user experience degradation based on user feedback.
本發明的一種預測無線網路的用戶體驗劣化的電子裝置,包含處理器以及收發器。處理器耦接收發器,其中處理器經配置以執行:通過收發器接收對應於用戶的用戶回饋資訊、用戶訊號品質資訊以及用戶網站瀏覽記錄;統計用戶回饋資訊中的預定義字串的數量以產生第一特徵資料;統計用戶網站瀏覽記錄中的預定義網站的數量以產生第二特徵資料;根據用戶訊號品質資訊計算訊號參數的平均值以產生第三特徵資料;將第一特徵資料、第二特徵資料以及第三特徵資料輸入至至少一機器學習模型以產生預測結果;以及通過收發器輸出對應於用戶的預測結果。An electronic device for predicting user experience degradation in a wireless network of the present invention includes a processor and a transceiver. The processor is coupled to the transceiver, wherein the processor is configured to perform: receiving user feedback information corresponding to the user, user signal quality information, and user website browsing records through the transceiver; counting the number of predefined word strings in the user feedback information and Generate the first characteristic data; count the number of predefined websites in the user’s website browsing records to generate the second characteristic data; calculate the average value of the signal parameters according to the user’s signal quality information to generate the third characteristic data; combine the first characteristic data, the second The second characteristic data and the third characteristic data are input into at least one machine learning model to generate a prediction result; and the prediction result corresponding to the user is output through the transceiver.
在本發明的一實施例中,上述的處理器更經配置以執行:通過收發器接收歷史用戶回饋資訊,其中歷史用戶回饋資訊包含分別對應於多個用戶的多個回饋訊息;以及根據多個回饋訊息的交集產生預定義字串。In an embodiment of the present invention, the above-mentioned processor is further configured to perform: receiving historical user feedback information through a transceiver, wherein the historical user feedback information includes a plurality of feedback messages respectively corresponding to a plurality of users; The intersection of the feedback messages produces a predefined string.
在本發明的一實施例中,上述的處理器更經配置以執行:通過收發器接收歷史用戶回饋資訊,其中歷史用戶回饋資訊包含分別對應於多個用戶的多個網站瀏覽記錄;以及根據多個網站瀏覽記錄的交集產生預定義網站。In an embodiment of the present invention, the above-mentioned processor is further configured to perform: receiving historical user feedback information through a transceiver, wherein the historical user feedback information includes a plurality of website browsing records respectively corresponding to a plurality of users; The intersection of website browsing records generates a predefined website.
在本發明的一實施例中,上述的至少一機器學習模型包含下列的至少其中之一:隨機森林模型、梯度提升樹以及神經網路。In an embodiment of the present invention, the above at least one machine learning model includes at least one of the following: random forest model, gradient boosting tree and neural network.
在本發明的一實施例中,上述的至少一機器學習模型根據第一特徵資料、第二特徵資料以及第三特徵資料輸出至少一機率值,其中處理器根據至少一機率值產生預測結果。In an embodiment of the present invention, the above at least one machine learning model outputs at least one probability value according to the first characteristic data, the second characteristic data and the third characteristic data, wherein the processor generates a prediction result according to the at least one probability value.
在本發明的一實施例中,上述的處理器響應於至少一機率值大於閾值而產生預測結果,其中預測結果指示用戶體驗劣化即將發生。In an embodiment of the present invention, the processor generates a prediction result in response to at least one probability value being greater than a threshold, wherein the prediction result indicates that user experience degradation is about to occur.
在本發明的一實施例中,上述的處理器更經配置以執行:通過收發器接收對應於歷史用戶體驗劣化的歷史用戶回饋資訊、歷史用戶訊號品質資訊以及歷史用戶網站瀏覽記錄;以及根據歷史用戶回饋資訊、歷史用戶訊號品質資訊以及歷史用戶網站瀏覽記錄訓練至少一機器學習模型。In an embodiment of the present invention, the above-mentioned processor is further configured to perform: receiving historical user feedback information, historical user signal quality information, and historical user website browsing records corresponding to historical user experience degradation through a transceiver; and according to the historical The user feedback information, historical user signal quality information and historical user website browsing records train at least one machine learning model.
在本發明的一實施例中,上述的處理器更經配置以執行:統計歷史用戶回饋資訊中的預定義字串的數量以產生第一歷史特徵資料;統計歷史用戶網站瀏覽記錄中的預定義網站的數量以產生第二歷史特徵資料;根據歷史用戶訊號品質資訊計算訊號參數的歷史平均值以產生第三歷史特徵資料;以及根據第一歷史特徵資料、第二歷史特徵資料以及第三歷史特徵資料訓練至少一機器學習模型。In an embodiment of the present invention, the above-mentioned processor is further configured to perform: counting the number of predefined character strings in historical user feedback information to generate first historical characteristic data; counting the predefined word strings in historical user website browsing records The number of websites to generate the second historical characteristic data; calculate the historical average value of signal parameters according to the historical user signal quality information to generate the third historical characteristic data; and according to the first historical characteristic data, the second historical characteristic data and the third historical characteristic data The data trains at least one machine learning model.
在本發明的一實施例中,上述的訊號參數包含下載速率以及上傳速率。In an embodiment of the present invention, the above signal parameters include a download rate and an upload rate.
本發明的一種預測無線網路的用戶體驗劣化的方法,包含:接收對應於用戶的用戶回饋資訊、用戶訊號品質資訊以及用戶網站瀏覽記錄;統計用戶回饋資訊中的預定義字串的數量以產生第一特徵資料;統計用戶網站瀏覽記錄中的預定義網站的數量以產生第二特徵資料;根據用戶訊號品質資訊計算訊號參數的平均值以產生第三特徵資料;將第一特徵資料、第二特徵資料以及第三特徵資料輸入至至少一機器學習模型以產生預測結果;以及輸出對應於用戶的預測結果。A method for predicting user experience degradation of a wireless network of the present invention includes: receiving user feedback information corresponding to the user, user signal quality information, and user website browsing records; counting the number of predefined word strings in the user feedback information to generate The first characteristic data; count the number of predefined websites in the user's website browsing record to generate the second characteristic data; calculate the average value of the signal parameters according to the user signal quality information to generate the third characteristic data; combine the first characteristic data, the second The characteristic data and the third characteristic data are input into at least one machine learning model to generate a prediction result; and output the prediction result corresponding to the user.
基於上述,本發明的電子裝置可根據用戶回饋資訊、用戶訊號品質資訊或用戶網站瀏覽記錄等回饋資訊來預測無線網路是否發生用戶體驗劣化。Based on the above, the electronic device of the present invention can predict whether user experience degradation occurs in the wireless network according to feedback information such as user feedback information, user signal quality information, or user website browsing records.
圖1根據本發明的一實施例繪示一種預測無線網路的用戶體驗劣化的電子裝置100的示意圖。FIG. 1 is a schematic diagram of an
電子裝置100例如是中央處理單元(central processing unit,CPU),或是其他可程式化之一般用途或特殊用途的微控制單元(micro control unit,MCU)、微處理器(microprocessor)、數位信號處理器(digital signal processor,DSP)、可程式化控制器、特殊應用積體電路(application specific integrated circuit,ASIC)、圖形處理器(graphics processing unit,GPU)、影像訊號處理器(image signal processor,ISP)、影像處理單元(image processing unit,IPU)、算數邏輯單元(arithmetic logic unit,ALU)、複雜可程式邏輯裝置(complex programmable logic device,CPLD)、現場可程式化邏輯閘陣列(field programmable gate array,FPGA)或其他類似元件或上述元件的組合。處理器110可耦接至儲存媒體120以及收發器120,並且存取和執行儲存於儲存媒體120中的多個模組和各種應用程式。The
儲存媒體120例如是任何型態的固定式或可移動式的隨機存取記憶體(random access memory,RAM)、唯讀記憶體(read-only memory,ROM)、快閃記憶體(flash memory)、硬碟(hard disk drive,HDD)、固態硬碟(solid state drive,SSD)或類似元件或上述元件的組合,而用於儲存可由處理器110執行的多個模組或各種應用程式。在本實施例中,儲存媒體120可儲存包含了一或多個機器學習模型的至少一機器學習模型121。The
收發器130以無線或有線的方式傳送及接收訊號。收發器130還可以執行例如低噪聲放大、阻抗匹配、混頻、向上或向下頻率轉換、濾波、放大以及類似的操作。The
處理器110可通過收發器130接收對應於用戶的用戶回饋資訊、用戶訊號品質資訊以及用戶網站瀏覽記錄等回饋資訊,其中所述回饋資訊可來自於用戶的客訴記錄或來自於電信業者的客服部門的資料。此外,處理器110可通過收發器130存取電信業者的伺服器,從而自伺服器取得用戶在特定時間段期間的用戶訊號品質資訊。用戶訊號品質資訊可包含一或多種訊號參數,其中訊號參數例如是下載速率或上傳速率等。The
表1為一客訴記錄的範例。處理器110可從表1的回饋內容中取得用戶回饋資訊,並可從表1的網站瀏覽記錄中取得對應於特定時間段期間的用戶網站瀏覽記錄。
表1
儲存媒體120可儲存預定義字串。處理器110可統計用戶回饋資訊中的預定義字串的數量以產生第一特徵資料。儲存媒體120中的預定義字串為可擴增的。表2為預定義字串的範例。以表1和表2為例,在表1中,預定義字串「持續30天以上」出現了1次,預定義字串「室內0~2格」出現了1次,預定義字串「室內3~5格」出現了0次,預定義字串「數據-上網速度慢」出現了1次,並且預定義字串「網路收訊很差」出現了1次。據此,處理器110可統計表1的回饋內容中出現的預定義字串的數量,以產生如表3所示的對應於用戶A的第一特徵資料。
表2
在一實施例中,處理器110可根據多個用戶回饋的資訊產生預定義字串。具體來說,處理器110可通過收發器130接收歷史用戶回饋資訊,其中歷史用戶回饋資訊可包含分別對應於多個用戶的多個回饋訊息。處理器110可根據多個回饋訊息的交集產生預定義字串。舉例來說,若有多個回饋訊息的交集包含「持續30天以上」的字串。處理器110可將「持續30天以上」的字串設定為預定義字串。In one embodiment, the
另一方面,儲存媒體120可儲存預定義網站。處理器110可統計用戶網站瀏覽記錄中的預定義網站的數量以產生第二特徵資料。儲存媒體120中的預定義網站為可擴增的。表4為預定義網站的範例。假設在表1中,用戶A的「用戶近30天內網站瀏覽記錄」出現過1次預定義網站「網站A」,出現過1次預定義網站「網站B」,並且出現過0次預定義網站「網站C」。據此,處理器110可統計「用戶近30天內網站瀏覽記錄」中的預定義網站的數量,以產生如表5所示的對應於用戶A的第二特徵資料。
表4
在一實施例中,處理器110可根據多個用戶回饋的資訊產生預定義網站。具體來說,處理器110可通過收發器130接收歷史用戶回饋資訊,其中歷史用戶回饋資訊可包含分別對應於多個用戶的多個網站瀏覽記錄。處理器110可根據多個網站瀏覽記錄的交集產生預定義網站。舉例來說,若有多個網站瀏覽記錄的交集包含「網站A」。處理器110可將「網站A」設定為預定義網站。處理器110可根據用戶訊號品質計算訊號參數的平均值以產生第三特徵資料,其中第三特徵資料可包含平均下載速率或平均上傳速率。In one embodiment, the
在取得第一特徵資料、第二特徵資料以及第三特徵資料後,處理器110可將第一特徵資料、第二特徵資料以及第三特徵資料輸入至至少一機器學習模型121以產生對應於用戶的預測結果。預測結果可指示無線網路的用戶(例如:用戶A)是否發生用戶體驗劣化的現象。處理器110可通過收發器130輸出預測結果以供電子裝置100的使用者(例如:維運人員)參考。After obtaining the first characteristic data, the second characteristic data and the third characteristic data, the
在一實施例中,至少一機器學習模型121可包含隨機森林模型(random forest)、梯度提升樹(gradient boost tree)或神經網路(neural network)。至少一機器學習模型121可根據第一特徵資料、第二特徵資料以及第三特徵資料產生至少一機率值。處理器110可根據至少一機率值產生預測結果。In one embodiment, at least one
在一實施例中,處理器110可響應於至少一機率值大於閾值而產生預測結果,其中所述預測結果指示用戶體驗劣化即將發生。舉例來說,假設至少一機器學習模型121包含一個機器學習模型,並且閾值為0.5。至少一機器學習模型121可根據第一特徵資料、第二特徵資料以及第三特徵資料產生一個機率值。若所述機率值大於0.5,則處理器110產生的預測結果可指示用戶體驗劣化即將發生。In one embodiment, the
在一實施例中,處理器110可計算至少一機率值的平均值,並可響應於平均值大於閾值而產生預測結果,其中所述預測結果指示用戶體驗劣化即將發生。舉例來說,假設至少一機器學習模型121包含兩個機器學習模型,並且閾值為0.5。所述兩個機器學習模型可分別根據第一特徵資料、第二特徵資料以及第三特徵資料產生機率值。處理器110可計算兩個機率值的平均值。若所述平均值大於0.5,則處理器110產生的預測結果可指示用戶體驗劣化即將發生。In one embodiment, the
在一實施例中,處理器110可根據用戶在先前回饋的資訊訓練至少一機器學習模型121。處理器110可通過收發器130接收對應於歷史用戶體驗劣化的歷史用戶回饋資訊、歷史用戶訊號品質資訊以及歷史用戶網站瀏覽記錄。處理器110可根據歷史用戶回饋資訊、歷史用戶訊號品質資訊以及歷史用戶網站瀏覽記錄訓練至少一機器學習模型121。In one embodiment, the
具體來說,處理器110可統計歷史用戶回饋資訊中的預定義字串的數量以產生第一歷史特徵資料。處理器110可統計歷史用戶網站瀏覽記錄中的預定義網站的數量以產生第二歷史特徵資料。處理器110可根據歷史用戶訊號品質資訊計算訊號參數的歷史平均值以產生第三歷史特徵資料。在取得第一歷史特徵資料、第二歷史特徵資料以及第三歷史特徵資料後,處理器110可將第一歷史特徵資料、第二歷史特徵資料以及第三歷史特徵資料作為訓練資料以訓練至少一機器學習模型121。Specifically, the
圖2根據本發明的一實施例繪示一種預測無線網路的用戶體驗劣化的方法的流程圖,其中所述方法可由如圖1所示的電子裝置100實施。在步驟S201中,接收對應於用戶的用戶回饋資訊、用戶訊號品質資訊以及用戶網站瀏覽記錄。在步驟S202中,統計用戶回饋資訊中的預定義字串的數量以產生第一特徵資料。在步驟S203中,統計用戶網站瀏覽記錄中的預定義網站的數量以產生第二特徵資料。在步驟S204中,根據用戶訊號品質資訊計算訊號參數的平均值以產生第三特徵資料。在步驟S205中,將第一特徵資料、第二特徵資料以及第三特徵資料輸入至至少一機器學習模型以產生預測結果。在步驟S206中,輸出對應於用戶的預測結果。FIG. 2 shows a flow chart of a method for predicting user experience degradation in a wireless network according to an embodiment of the present invention, wherein the method can be implemented by the
綜上所述,本發明的電子裝置可根據用戶回饋資訊、用戶訊號品質資訊或用戶網站瀏覽記錄等回饋資訊來預測無線網路是否發生用戶體驗劣化。電子裝置可通過諸如隨機森林模型、梯度提升樹或神經網路等機器學習模型來執行用戶體驗劣化之預測。相較於傳統使用基地台的性能指標分析基地台的故障,本發明的電子裝置可產生與用戶的回饋直接地相關的用戶體驗劣化之預測結果,藉以輔助無線網路的維運人員盡速排除可能造成用戶體驗劣化的根因,從而改善客戶的用戶體驗。To sum up, the electronic device of the present invention can predict whether user experience degradation occurs in a wireless network according to feedback information such as user feedback information, user signal quality information, or user website browsing records. The electronic device can perform prediction of user experience degradation through machine learning models such as random forest models, gradient boosting trees, or neural networks. Compared with the traditional use of performance indicators of base stations to analyze the faults of base stations, the electronic device of the present invention can generate prediction results of user experience degradation directly related to user feedback, so as to assist wireless network maintenance personnel to eliminate as soon as possible Possible root causes of user experience degradation, thereby improving the customer's user experience.
100:電子裝置 110:處理器 120:儲存媒體 121:至少一機器學習模型 130:收發器 S201、S202、S203、S204、S205、S206:步驟100: Electronic device 110: Processor 120: storage media 121:At least one machine learning model 130: Transceiver S201, S202, S203, S204, S205, S206: steps
圖1根據本發明的一實施例繪示一種預測無線網路的用戶體驗劣化的電子裝置的示意圖。 圖2根據本發明的一實施例繪示一種預測無線網路的用戶體驗劣化的方法的流程圖。 FIG. 1 is a schematic diagram of an electronic device for predicting user experience degradation of a wireless network according to an embodiment of the present invention. FIG. 2 shows a flow chart of a method for predicting user experience degradation in a wireless network according to an embodiment of the present invention.
S201、S202、S203、S204、S205、S206:步驟 S201, S202, S203, S204, S205, S206: steps
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