參照附圖，通過下面的說明書，本發明的前述以及其它特徵將變得明顯。在說明書和附圖中，具體公開了本發明的特定實施方式，其表明了其中可以採用本發明的原則的部分實施方式，應瞭解的是，本發明不限於所描述的實施方式，相反，本發明包括落入所附申請專利範圍的範圍內的全部修改、變型以及等同物。
在本說明書實施例中，術語“第一”、“第二”等用於對不同元素從稱謂上進行區分，但並不表示這些元素的空間排列或時間順序等，這些元素不應被這些術語所限制。術語“及/或”包括相關聯列出的術語的一種或多個中的任何一個和所有組合。
在本說明書實施例中，單數形式“一”、“該”等包括複數形式，應廣義地理解為“一種”或“一類”而並不是限定為“一個”的含義；此外術語“所述”應理解為既包括單數形式也包括複數形式，除非上下文另外明確指出。此外術語“根據”應理解為“至少部分根據……”，術語“基於”應理解為“至少部分基於……”，除非上下文另外明確指出。
下面結合附圖對本說明書實施例的具體實施方式進行說明。
實施例1
本實施例1提供一種價格資訊的處理方法。圖1是本實施例的價格資訊的處理方法的一個示意圖。
如圖1所述，該方法包括：
步驟101，從包含與第一貨幣相關的第一價格資訊的影像中識別該第一價格資訊；
步驟102，根據該第一價格資訊以及該第一貨幣與預設的第二貨幣之間的轉換關係，確定與該第二貨幣相關的第二價格資訊；
步驟103，顯示該第二價格資訊。
通過本實施例的上述方法，能夠從影像中自動識別第一價格資訊並進行貨幣轉換，以將貨幣轉換後的第二價格資訊顯示給使用者，操作簡單，且避免了誤操作，提升使用者體驗。
在本實施例中，第一貨幣、第二貨幣是不同類型的貨幣，例如，第一貨幣是日元、第二貨幣是人民幣。
在本實施例中，第一貨幣可以是使用者當前所處的國家或地區的貨幣，第二貨幣可以是預設的貨幣，例如，使用者所屬國的貨幣或者使用者想要用於衡量商品價格的貨幣。該第一貨幣可由使用者設置，也可以由設備自動獲取。關於設備自動獲取第一貨幣的具體方式，可參見後述的說明。
與第一貨幣相關的第一價格資訊是以第一貨幣計價的價格資訊。同樣地，與第二貨幣相關的第二價格資訊是以第二貨幣計價的價格資訊。這樣，第一貨幣與第二貨幣之間的轉換關係即不同貨幣之間的匯率關係。
在本實施例中，該第一價格資訊包括價格，或者，價格以及相應的折扣資訊。也就是說，該第一價格資訊是能夠用於確定與第一貨幣相關的第一價格的資訊。
例如，通過將價格和折扣相乘，得到以第一貨幣表示的實際價格。
在本實施例中，包含第一價格資訊的影像可通過任意方式獲得。例如，使用者使用終端設備對包含第一價格資訊的實景中的區域進行影像採集而獲得。這裡的實景例如是包含價簽或者價格文案等的實際生活場景。這裡的價簽例如可以是商場中商品上粘貼的價簽，價格文案例如可以是購物網站上在商品頁面中顯示的與價格相關的文字或圖片等。
在本實施例中，在上述步驟101中，可以使用任意方式從影像中識別第一價格資訊。例如，可以通過使用機器學習而獲得的識別模型進行識別的方式，但本實施例不以此作為限制。
在本實施例中，在上述步驟103中，還可以同時顯示第一貨幣與第二貨幣之間的轉換關係，即，可以同時顯示匯率資訊。
圖2是本實施例的價格資訊的處理方法的另一個示意圖。如圖2所示，除上述步驟101-103以外，該方法還可以包括：
步驟201，使用訓練樣本訓練用於識別該第一價格資訊的識別模型。
由此，上述步驟101能夠基於該識別模型從影像中識別第一價格資訊。
該步驟201是可選的。另外，該步驟201可以是預先進行的。也就是說，在實際的識別之前預先訓練並得到識別模型。
在本實施例中，上述訓練樣本可以是大量包含價格資訊或類似資訊的圖片。包含類似資訊的圖片例如可以是包含數位的其他圖片，例如包含數學公式的圖片、日曆的圖片等。
在本實施例中，如圖2所示，該方法還可以包括：
步驟202，將包含第一價格資訊的影像添加到上述訓練樣本中，基於添加後的訓練樣本對識別模型進行重新訓練，以獲得更新後的識別模型。
由此，能夠利用該影像向訓練樣本提供反饋樣本，從而提高基於更新後的識別模型進行識別時的識別度。
上述步驟202中獲得的更新後的識別模型可被提供給步驟101，用於基於該更新後的識別模型從該影像中識別第一價格資訊。
該步驟202是可選的，可不執行步驟202而在步驟201後直接執行步驟101。
另外，在實施步驟202時，可以在獲得包含第一價格資訊的影像後直接實施步驟202，也可以在無法從包含第一價格資訊的影像中識別第一價格資訊的情況下實施步驟202。
在本實施例中，上述識別模型可以是基於卷積神經網路的模型，也可以是其他模型。
在本實施例中，該方法還可以包括步驟(未圖示)：使用測試樣本對該識別模型進行測試，將該影像添加到測試樣本中，並且基於添加後的測試樣本對該識別模型進行重新測試，以獲得更新後的識別模型。該步驟為可選的步驟。
由此，能夠利用該影像向測試樣本提供反饋樣本，從而提高基於更新後的識別模型進行識別時的識別度。
在本實施例中，測試樣本也可以是大量包含價格資訊或類似資訊的圖片。
在本實施例中，可以在獲得包含第一價格資訊的影像後直接將該影像添加到測試樣本中，也可以在無法從該影像中識別第一價格資訊的情況下才將該影像添加到測試樣本中。
如果採用在無法從該影像中識別第一價格資訊的情況下才將該影像添加到訓練樣本及/或測試樣本中的方案，還可在基於更新後的識別模型的測試結果滿足預設條件的情況下重新從該影像中識別第一價格資訊。由此，能夠提高從該影像中識別出該第一價格資訊的可能性。
在本實施例中，該方法還可包括步驟：獲取使用者的定位資訊，根據該定位資訊確定該第一貨幣，並基於確定的該第一貨幣和預設的第二貨幣獲取該轉換關係。由此，能夠由設備自動獲取該第一貨幣，進一步簡化了操作並避免誤操作。該步驟是可選的步驟。
例如，根據使用者的定位資訊，確定該使用者當前所處的國家或地區，該第一貨幣則是該國家或地區的貨幣。
該轉換關係例如可以通過向提供實時匯率資訊服務的伺服器查詢而獲得。
在本實施例中，上述方法可以在一個設備上實現，例如可以在一個終端設備中實現。也可以分散式地在多個設備上實現，例如可以在終端設備和伺服器上共同執行。但本實施例不限於上述示例，也可以在其他類型的設備上執行。
在多個設備上實現時，上述步驟101-103例如可通過如下方式實現：可在第一設備(例如終端設備)中識別第一價格資訊並獲取定位資訊，並將該兩種資訊聚合，將聚合後的資訊發送到提供貨幣轉換服務的第二設備(例如伺服器)。該第二設備可從聚合後的資訊中提取出定位資訊和第一價格資訊，根據該定位資訊確定第一貨幣，並基於該第一貨幣和預設的第二貨幣獲取第一貨幣與第二貨幣之間的轉換關係，並且，根據提取出的第一價格資訊以及該轉換關係，確定與第二貨幣相關的第二價格資訊，將該第二價格資訊發送到第一設備進行顯示。但本實施例不以此作為限制，也可以採用其他類似的方式實現。
在多個設備上實現時，上述步驟201-202、101-103例如可通過如下方式實現：可在第三設備(例如伺服器)上執行步驟201-202，在第一設備(例如終端設備上)執行步驟101-103。另外，上述對該識別模型進行測試，將該影像添加到測試樣本中，並且基於添加後的測試樣本對該識別模型進行重新測試，以獲得更新後的識別模型的步驟也可在上述第三設備上執行。在基於更新後的識別模型的測試結果滿足預設條件的情況下重新從該影像中識別第一價格資訊的步驟可在第一設備上執行。識別模型或更新後的識別模型在被獲得後可從第三設備被發送到第一設備，第一設備可基於接收到的識別模型或更新後的識別模型識別第一價格資訊。第一設備可將包含第一價格資訊的影像發送到第三設備用於添加到到訓練樣本及/或測試樣本。但本實施例不以此作為限制，也可以採用其他類似的方式實現。
上述第二設備和第三設備可以是同一設備，也可以是不同的設備。
在本實施例中，在上述第一設備為終端設備，上述第三設備為伺服器的情況下，可將終端設備設置無需使用者允許即可從終端設備向伺服器發送包含第一價格資訊的影像，也可設置為在使用者允許的情況下才能從終端設備向伺服器發送該影像，否則不能發送該影像。
通過本實施例的價格資訊的處理方法，能夠自動識別價格資訊並進行貨幣轉換，操作簡單，且避免了誤操作，提升使用者體驗。
實施例2
本實施例2提供一種價格資訊的處理裝置。圖3是本實施例的價格資訊的處理裝置300的一個示意圖。本實施例2中與實施例1相同的部分不再贅述。
如圖3所示，價格資訊的處理裝置300包括識別單元301、確定單元302和顯示單元303。識別單元301從包含與第一貨幣相關的第一價格資訊的影像中識別該第一價格資訊；確定單元302根據該第一價格資訊以及該第一貨幣與第二貨幣之間的轉換關係，確定與該第二貨幣相關的第二價格資訊；顯示單元303顯示該第二價格資訊。
在本實施例中，該第一價格資訊可包括價格及/或相應的折扣資訊。
在本實施例中，處理裝置300還可包括：定位單元304、貨幣確定單元305和轉換關係獲取單元306。定位單元304獲取定位資訊；貨幣確定單元305根據該定位資訊確定該第一貨幣；轉換關係獲取單元306基於確定的該第一貨幣和預設的第二貨幣獲取該轉換關係。
上述定位單元304、貨幣確定單元305和轉換關係獲取單元306是可選的部件。
在本實施例中，處理裝置300還可包括訓練單元307，訓練單元307使用訓練樣本訓練用於識別第一價格資訊的識別模型。
上述訓練單元307是可選的部件。
在本實施例中，該識別模型可以為卷積神經網路模型。
在本實施例中，處理裝置300還可包括添加單元308，添加單元308將包含第一價格資訊的影像添加到該訓練樣本中，基於添加後的訓練樣本對該識別模型進行重新訓練，以獲得更新後的識別模型。
上述添加單元308是可選的部件。
在本實施例中，處理裝置300還可包括測試單元309，測試單元309使用測試樣本對識別模型進行測試，添加單元308還可將包含第一價格的影像添加到該測試樣本中，並且基於添加後的測試樣本對該識別模型進行重新測試，以獲得更新後的識別模型。
上述測試單元309是可選的部件。
在本實施例中，處理裝置300還可包括影像取得單元310，該影像取得單元310取得包含第一價格資訊的影像。並且，添加單元308還可從影像取得單元310獲得該影像。
上述影像取得單元310是可選的部件。
上述各單元的功能可通過實施例1中所述的具體方式實現。此外，上述各單元可通過一個設備實現,也可通過多個設備實現，相應的具體實現方式可參見實施例1。此處不再贅述。
通過本實施例的價格資訊的處理裝置，能夠自動識別價格資訊並進行貨幣轉換，操作簡單，且避免了誤操作，提升使用者體驗。
本說明書實施例還提供一種電腦可讀程式，該電腦可讀程式使得處理器執行實施例1所述的方法。
本說明書實施例還提供一種儲存有電腦可讀程式的儲存媒體，其中，儲存有電腦可讀程式的儲存媒體，該電腦可讀程式使得處理器執行實施例1所述的方法。
結合本說明書實施例描述的價格資訊的處理裝置可直接體現為硬體、由處理器執行的軟體模組或二者組合。例如，附圖中所示的功能方塊圖中的一個或多個及/或功能方塊圖的一個或多個組合，既可以對應於電腦程式流程的各個軟體模組，亦可以對應於各個硬體模組。這些軟體模組，可以分別對應於附圖所示的各個步驟。這些硬體模組例如可利用現場可程式閘陣列(FPGA)將這些軟體模組固化而實現。
軟體模組可以位於RAM記憶體、快閃記憶體、ROM記憶體、EPROM記憶體、EEPROM記憶體、暫存器、硬碟、行動磁碟、CD-ROM或者本領域已知的任何其它形式的儲存媒體。可以將一種儲存媒體耦接至處理器，從而使處理器能夠從該儲存媒體讀取資訊，且可向該儲存媒體寫入資訊；或者該儲存媒體可以是處理器的組成部分。處理器和儲存媒體可以位於ASIC中。該軟體模組可以儲存在電子設備的記憶體中，也可以儲存在可插入電子設備的儲存卡中。例如，若電子設備(例如行動終端)採用的是較大容量的MEGA-SIM卡或者大容量的快閃記憶體裝置，則該軟體模組可儲存在該MEGA-SIM卡或者大容量的快閃記憶體裝置中。
針對圖描述的功能方塊圖中的一個或多個及/或功能方塊圖的一個或多個組合，可以實現為用於執行本發明所描述功能的通用處理器、數位信號處理器(DSP)、特殊應用積體電路(ASIC)、現場可程式閘陣列(FPGA)或其它可編程邏輯裝置、分散式閘極或電晶體邏輯裝置、分立硬體組件、或者其任意適當組合。針對附圖描述的功能方塊圖中的一個或多個及/或功能方塊圖的一個或多個組合，還可以實現為計算設備的組合，例如，DSP和微處理器的組合、多個微處理器、與DSP通信結合的一個或多個微處理器或者任何其它這種配置。
此外，本說明書實施例的處理方法、處理裝置可在單處理器環境中實現，也可在多處理器的環境中分散式地實現。
以上結合具體的實施方式對本發明進行了描述，但本領域技術人員應該清楚，這些描述都是示例性的，並不是對本發明保護範圍的限制。本領域技術人員可以根據本發明的原理對本發明做出各種變型和修改，這些變型和修改也在本發明的範圍內。The foregoing and other features of the present invention will become apparent from the following description with reference to the drawings. In the specification and the drawings, specific embodiments of the present invention are disclosed in detail, which show some of the embodiments in which the principles of the present invention can be adopted. It should be understood that the present invention is not limited to the described embodiments. The invention includes all modifications, variations, and equivalents that fall within the scope of the attached patent application.
In the embodiments of this specification, the terms "first", "second", etc. are used to distinguish different elements from their titles, but they do not mean the spatial arrangement or chronological order of these elements, and these elements should not be used by these terms Restricted. The term "and/or" includes any and all combinations of one or more of the associated listed terms.
In the embodiments of the present specification, the singular forms "a", "the", etc. include plural forms, which should be broadly understood as "a" or "a class" rather than being limited to the meaning of "a"; in addition, the term "said" It should be understood to include both singular and plural forms unless the context clearly indicates otherwise. In addition, the term "based on" should be understood as "based at least in part on..." and the term "based on" should be understood as "based at least in part on..." unless the context clearly indicates otherwise.
The following describes the specific implementation manners of the embodiments of the present specification with reference to the drawings.
Example 1
The first embodiment provides a method for processing price information. FIG. 1 is a schematic diagram of the price information processing method of this embodiment.
As shown in FIG. 1, the method includes:
Step 101: Identify the first price information from the image containing the first price information related to the first currency;
Step 102: Determine second price information related to the second currency according to the first price information and the conversion relationship between the first currency and the preset second currency;
Step 103: Display the second price information.
Through the above method of this embodiment, the first price information can be automatically recognized from the image and currency conversion can be performed to display the second price information after currency conversion to the user, the operation is simple, and the wrong operation is avoided, and the user experience is improved .
In this embodiment, the first currency and the second currency are different types of currencies. For example, the first currency is Japanese yen and the second currency is Renminbi.
In this embodiment, the first currency may be the currency of the country or region where the user is currently located, and the second currency may be the default currency, for example, the currency of the country where the user belongs or the user wants to use to measure the commodity Price currency. The first currency can be set by the user, or can be automatically obtained by the device. For the specific method for the device to automatically acquire the first currency, please refer to the following description.
The first price information related to the first currency is price information denominated in the first currency. Similarly, the second price information related to the second currency is price information denominated in the second currency. In this way, the conversion relationship between the first currency and the second currency is the exchange rate relationship between different currencies.
In this embodiment, the first price information includes price, or, price and corresponding discount information. That is, the first price information is information that can be used to determine the first price related to the first currency.
For example, by multiplying the price and the discount, the actual price expressed in the first currency is obtained.
In this embodiment, the image containing the first price information can be obtained in any way. For example, a user uses a terminal device to acquire an image in a real scene containing first price information. The real scene here is, for example, a real life scene including price tags or price copy. The price tag here may be, for example, a price tag pasted on a commodity in a shopping mall, and the price copy may be, for example, a price-related text or picture displayed on a product page on a shopping website.
In this embodiment, in the above step 101, the first price information can be identified from the image in any manner. For example, it is possible to recognize by using a recognition model obtained by machine learning, but this embodiment is not limited thereto.
In this embodiment, in the above step 103, the conversion relationship between the first currency and the second currency can also be displayed at the same time, that is, the exchange rate information can be displayed at the same time.
FIG. 2 is another schematic diagram of the price information processing method of this embodiment. As shown in FIG. 2, in addition to the above steps 101-103, the method may further include:
Step 201: Use a training sample to train a recognition model for identifying the first price information.
Therefore, the above step 101 can recognize the first price information from the video based on the recognition model.
This step 201 is optional. In addition, this step 201 may be performed in advance. In other words, the recognition model is pre-trained and obtained before the actual recognition.
In this embodiment, the training sample may be a large number of pictures containing price information or similar information. Pictures containing similar information may be other pictures containing digits, such as pictures containing mathematical formulas, pictures of calendars, etc.
In this embodiment, as shown in FIG. 2, the method may further include:
Step 202: Add an image containing the first price information to the above training sample, and retrain the recognition model based on the added training sample to obtain an updated recognition model.
As a result, it is possible to use the image to provide feedback samples to the training samples, thereby improving the recognition degree when performing recognition based on the updated recognition model.
The updated identification model obtained in step 202 above may be provided to step 101 for identifying the first price information from the image based on the updated identification model.
This step 202 is optional, and step 101 may be directly executed after step 201 without performing step 202.
In addition, when implementing step 202, step 202 may be implemented directly after obtaining the image containing the first price information, or step 202 may be implemented when the first price information cannot be recognized from the image containing the first price information.
In this embodiment, the above recognition model may be a model based on a convolutional neural network, or other models.
In this embodiment, the method may further include the step (not shown): test the identification model using a test sample, add the image to the test sample, and re-based the identification model based on the added test sample Test to obtain an updated recognition model. This step is optional.
As a result, it is possible to use the image to provide feedback samples to the test samples, thereby improving the recognition degree when performing recognition based on the updated recognition model.
In this embodiment, the test sample may also be a large number of pictures containing price information or similar information.
In this embodiment, the image containing the first price information may be directly added to the test sample after obtaining the image, or the image may be added to the test only when the first price information cannot be recognized from the image In the sample.
If the solution of adding the image to the training sample and/or test sample only when the first price information cannot be recognized from the image, the test result based on the updated recognition model can also meet the preset conditions In this case, re-recognize the first price information from the image. Thereby, the possibility of recognizing the first price information from the video can be increased.
In this embodiment, the method may further include the steps of acquiring positioning information of the user, determining the first currency based on the positioning information, and acquiring the conversion relationship based on the determined first currency and the preset second currency. Thus, the first currency can be automatically acquired by the device, which further simplifies the operation and avoids erroneous operation. This step is optional.
For example, according to the user's location information, the country or region where the user is currently located is determined, and the first currency is the currency of the country or region.
The conversion relationship can be obtained by querying a server that provides real-time exchange rate information services, for example.
In this embodiment, the above method may be implemented on one device, for example, a terminal device. It can also be implemented on multiple devices in a decentralized manner, for example, it can be executed jointly on the terminal device and the server. However, this embodiment is not limited to the above example, and can also be executed on other types of devices.
When implemented on multiple devices, the above steps 101-103 may be implemented, for example, in the following manner: the first price information may be identified in the first device (eg, terminal device) and positioning information may be obtained, and the two types of information may be aggregated to The aggregated information is sent to a second device (such as a server) that provides currency conversion services. The second device can extract positioning information and first price information from the aggregated information, determine the first currency based on the positioning information, and obtain the first currency and the second currency based on the first currency and the preset second currency The conversion relationship between currencies, and based on the extracted first price information and the conversion relationship, determine the second price information related to the second currency, and send the second price information to the first device for display. However, this embodiment is not limited as such, and may also be implemented in other similar ways.
When implemented on multiple devices, the above steps 201-202, 101-103 may be implemented, for example, by performing the steps 201-202 on a third device (such as a server) and on the first device (such as a terminal device) ) Perform steps 101-103. In addition, the step of testing the recognition model described above, adding the image to the test sample, and retesting the recognition model based on the added test sample to obtain the updated recognition model may also be performed on the third device On the implementation. The step of re-recognizing the first price information from the image under the condition that the test result based on the updated recognition model satisfies the preset condition may be performed on the first device. The identification model or the updated identification model may be sent from the third device to the first device after being obtained, and the first device may identify the first price information based on the received identification model or the updated identification model. The first device may send the image containing the first price information to the third device for addition to training samples and/or test samples. However, this embodiment is not limited as such, and may also be implemented in other similar ways.
The above-mentioned second device and third device may be the same device or different devices.
In this embodiment, when the first device is a terminal device and the third device is a server, the terminal device can be set to send the first price information from the terminal device to the server without user permission The image can also be set to send the image from the terminal device to the server with permission of the user, otherwise the image cannot be sent.
Through the processing method of price information in this embodiment, the price information can be automatically recognized and currency conversion is performed, the operation is simple, and the misoperation is avoided, and the user experience is improved.
Example 2
The second embodiment provides a price information processing device. FIG. 3 is a schematic diagram of the price information processing device 300 of this embodiment. The same parts of Embodiment 2 as Embodiment 1 will not be repeated.
As shown in FIG. 3, the price information processing device 300 includes an identification unit 301, a determination unit 302, and a display unit 303. The identifying unit 301 identifies the first price information from the image containing the first price information related to the first currency; the determining unit 302 determines the first price information and the conversion relationship between the first currency and the second currency The second price information related to the second currency; the display unit 303 displays the second price information.
In this embodiment, the first price information may include price and/or corresponding discount information.
In this embodiment, the processing device 300 may further include: a positioning unit 304, a currency determination unit 305, and a conversion relationship acquisition unit 306. The positioning unit 304 acquires positioning information; the currency determination unit 305 determines the first currency based on the positioning information; and the conversion relationship acquisition unit 306 acquires the conversion relationship based on the determined first currency and a preset second currency.
The above-mentioned positioning unit 304, currency determination unit 305, and conversion relationship acquisition unit 306 are optional components.
In this embodiment, the processing device 300 may further include a training unit 307, which uses the training samples to train the recognition model for recognizing the first price information.
The training unit 307 described above is an optional component.
In this embodiment, the recognition model may be a convolutional neural network model.
In this embodiment, the processing device 300 may further include an adding unit 308 that adds the image containing the first price information to the training sample, and retrains the recognition model based on the added training sample to obtain The updated recognition model.
The above adding unit 308 is an optional component.
In this embodiment, the processing device 300 may further include a test unit 309, which uses the test sample to test the recognition model, and the addition unit 308 may also add an image containing the first price to the test sample, and based on the addition The later test sample retests the recognition model to obtain an updated recognition model.
The above test unit 309 is an optional component.
In this embodiment, the processing device 300 may further include an image acquisition unit 310 that acquires an image including first price information. In addition, the adding unit 308 can also obtain the image from the image obtaining unit 310.
The image acquisition unit 310 described above is an optional component.
The functions of the above units can be implemented in the specific manner described in Embodiment 1. In addition, the above units may be implemented by one device, or by multiple devices. For a specific implementation manner, refer to Embodiment 1. I won't repeat them here.
The price information processing device of this embodiment can automatically recognize the price information and perform currency conversion. The operation is simple, and the erroneous operation is avoided, and the user experience is improved.
The embodiments of the present specification also provide a computer-readable program that causes the processor to execute the method described in Embodiment 1.
The embodiments of the present specification also provide a storage medium storing a computer-readable program, wherein the storage medium storing the computer-readable program causes the processor to execute the method described in Embodiment 1.
The price information processing device described in conjunction with the embodiments of the present specification may be directly embodied as hardware, a software module executed by a processor, or a combination of both. For example, one or more of the functional block diagrams shown in the drawings and/or one or more combinations of the functional block diagrams can correspond to each software module of the computer program flow or each hardware Module. These software modules can correspond to the steps shown in the drawings. These hardware modules can be realized by curing these software modules by using a field programmable gate array (FPGA), for example.
The software module may be located in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, register, hard disk, mobile disk, CD-ROM, or any other form known in the art Storage media. A storage medium may be coupled to the processor, so that the processor can read information from the storage medium and write information to the storage medium; or the storage medium may be an integral part of the processor. The processor and the storage medium may be located in the ASIC. The software module can be stored in the memory of the electronic device, or it can be stored in a memory card that can be inserted into the electronic device. For example, if an electronic device (such as a mobile terminal) uses a larger-capacity MEGA-SIM card or a larger-capacity flash memory device, the software module can be stored in the MEGA-SIM card or a larger-capacity flash Memory device.
One or more of the functional block diagrams described in the drawings and/or one or more combinations of the functional block diagrams may be implemented as a general-purpose processor, a digital signal processor (DSP) for performing the functions described in the present invention, Special application integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, decentralized gate or transistor logic devices, discrete hardware components, or any suitable combination thereof. One or more of the functional block diagrams described in the drawings and/or one or more combinations of the functional block diagrams can also be implemented as a combination of computing devices, for example, a combination of DSP and microprocessor, multiple microprocessing Processor, one or more microprocessors in communication with the DSP, or any other such configuration.
In addition, the processing method and the processing apparatus of the embodiments of the present specification may be implemented in a single-processor environment, or may be implemented in a distributed manner in a multi-processor environment.
The present invention has been described above in conjunction with specific embodiments, but it should be clear to those skilled in the art that these descriptions are exemplary and do not limit the protection scope of the present invention. Those skilled in the art can make various variations and modifications to the present invention based on the principles of the present invention, and these variations and modifications are also within the scope of the present invention.
