WO2021159745A1 - 一种数据处理方法、装置、设备及介质 - Google Patents
一种数据处理方法、装置、设备及介质 Download PDFInfo
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Classifications
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/26—Speech to text systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/284—Lexical analysis, e.g. tokenisation or collocates
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/008—Artificial life, i.e. computing arrangements simulating life based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. based on robots replicating pets or humans in their appearance or behaviour
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/161—Detection; Localisation; Normalisation
- G06V40/166—Detection; Localisation; Normalisation using acquisition arrangements
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/02—Feature extraction for speech recognition; Selection of recognition unit
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
- G10L2015/223—Execution procedure of a spoken command
Definitions
- This application relates to voice processing technology in artificial intelligence, and in particular to a data processing method, device, equipment, and medium.
- video robot calls are used in many industries, such as business consultation and business processing in the service industry.
- Video robot calls have gradually replaced manual labor, and can achieve business processing anytime, anywhere.
- the inventor realizes that when a user calls a video robot, different recognition engines are usually connected according to the different services that the user needs to handle, and the recognition engines are used to process the services. Since different services need to be processed by different servers, video robots need to carry more service attributes to connect with different recognition engines. Each service requires custom development of different recognition engines, which wastes a lot of resources and is costly.
- the embodiments of the present application provide a data processing method, device, equipment, and medium, which can avoid waste of resources and reduce costs.
- the embodiments of the present application provide a data processing method, including: acquiring first multimedia data about a first service from a terminal; identifying the first multimedia data to obtain the first service attribute information,
- the first service attribute information includes at least one of the service level of the first service or the business income of the first service; the recognition engine that matches the first service attribute information is determined from the shared recognition engine set as the target recognition Engine; output prompt information about processing the first service; obtain the second multimedia data sent for the prompt information from the terminal; send the second multimedia data to the first service platform, so that the The first service platform uses the target recognition engine to recognize the second multimedia data and process the first service.
- the embodiments of the present application provide a data processing device, including: a first acquisition module, configured to acquire first multimedia data related to a first service from a terminal; Volume data to obtain the first business attribute information, the first business attribute information includes at least one of the business level of the first business or the business income of the first business; the engine determination module is used to identify from the shared The recognition engine that matches the first service attribute information is determined in the engine set as the target recognition engine; the information output module is used to output prompt information about processing the first service; the second acquisition module is used to obtain information from the terminal The second multimedia data sent in response to the prompt information; the service processing module is configured to send the second multimedia data to the first service platform, so that the first service platform uses the target recognition engine for the first service platform Second, the multimedia data is identified, and the first service is processed.
- a first acquisition module configured to acquire first multimedia data related to a first service from a terminal
- Volume data to obtain the first business attribute information includes at least one of the business level of the first business or the business income of the first business
- One aspect of the present application provides a computer device, including: a processor, a memory, and a network interface; the processor is connected to the memory and the network interface, wherein the network interface is used to provide data communication functions, and the memory is used to store computer programs,
- the above-mentioned processor is configured to call the above-mentioned computer program to execute the following method: obtain the first multimedia data about the first service from the terminal; identify the first multimedia data to obtain the first service attribute information, the The first service attribute information includes at least one of the service level of the first service or the business income of the first service; the recognition engine matching the first service attribute information is determined from the shared recognition engine set as the target recognition engine ; Output prompt information about processing the first service; obtain the second multimedia data sent for the prompt information from the terminal; send the second multimedia data to the first service platform, so that the first A service platform uses the target recognition engine to recognize the second multimedia data and process the first service.
- One aspect of the embodiments of the present application provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and the computer program includes program instructions that, when executed by a processor, cause the processor to perform the following method : Obtain the first multimedia data about the first service from the terminal; identify the first multimedia data to obtain the first service attribute information, and the first service attribute information includes the service level of the first service Or at least one of the business income of the first service; determine the recognition engine matching the attribute information of the first service from the shared recognition engine set as the target recognition engine; output prompt information about processing the first service; The terminal obtains the second multimedia data sent for the prompt information; sends the second multimedia data to the first service platform, so that the first service platform uses the target recognition engine to perform the second multimedia data The media data is identified, and the first service is processed.
- the embodiments of the present application can avoid waste of resources, save investment in hardware resources, and thereby save costs. Further, it is possible to realize the separation of the two processes of determining the recognition engine and processing the business, and realize the rapid connection to the business processing platform for business processing.
- FIG. 1 is a schematic flowchart of a data processing method provided by an embodiment of the present application.
- Fig. 2 is a schematic flowchart of a data processing method provided by an embodiment of the present application.
- FIG. 3 is a schematic diagram of the composition structure of a data processing device provided by an embodiment of the present application.
- FIG. 4 is a schematic diagram of the composition structure of a computer device provided by an embodiment of the present application.
- the technical solution of this application can be applied to the fields of artificial intelligence, blockchain and/or big data technology to realize business processing.
- Artificial intelligence technology is a comprehensive discipline, covering a wide range of fields, including both hardware-level technology and software-level technology.
- Basic artificial intelligence technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, and mechatronics.
- Artificial intelligence software technology mainly includes computer vision technology, speech processing technology, natural language processing technology, and machine learning/deep learning.
- speech processing technology Speech Technology
- key technologies include automatic speech recognition technology (ASR), speech synthesis technology (TTS) and voiceprint recognition technology.
- ASR automatic speech recognition technology
- TTS speech synthesis technology
- voiceprint recognition technology Enabling computers to be able to listen, see, speak, and feel is the future development direction of human-computer interaction, among which voice has become one of the most promising human-computer interaction methods in the future.
- This application relates to the voice processing technology in artificial intelligence.
- the voice processing technology is used to recognize the first multimedia data about the first service to obtain the first service attribute information, and the first service attribute information is determined from the shared recognition engine set.
- the matched target recognition engine sends the second multimedia data to the first service platform, so that the first service platform uses the target recognition engine to recognize the second multimedia data and process the first service. Since different services in this application can share the recognition engines in the set, there is no need to customize recognition engines for different services, which can avoid waste of resources, save investment in hardware resources, and thereby save costs.
- This application can be applied to the fields of smart government affairs, smart education, etc., and is conducive to promoting the construction of smart cities.
- the technical solution of the present application is applicable to the scenario where the multimedia data sent by the terminal is recognized, so as to perform corresponding service processing according to the service attribute information in the multimedia data.
- the technical solution of this application is applicable to scenarios such as remote face-to-face audits, video return visits, and remote account opening.
- the media data is sent to the service platform corresponding to the first service, so that the service platform uses the target recognition engine to identify the second multimedia data and process the first service.
- the service attribute information in the multimedia data can be determined, so that the corresponding service can be handled according to the service attribute information.
- Figure 1 is a schematic flow chart of a data processing method provided by an embodiment of the present application.
- the method can be applied to computer equipment.
- Mobile Internet equipment MID, mobile internet device
- POS Point Of Sales, point of sale
- wearable devices such as smart watches, smart bracelets, etc.
- the method includes the following steps.
- the terminal may refer to a terminal used by a user for service processing.
- Terminals can include mobile phones, tablets, laptops, handheld computers, smart speakers, mobile Internet devices (MID, mobile internet device), POS (Point Of Sales, point of sale) machines, wearable devices (such as smart watches, smart bracelets, etc.), etc.
- the first business may include the business that the user needs to handle, such as purchasing XX property insurance, bank loans, bank card processing, credit card processing, and so on.
- the first service may also include services required by the user, such as bank card balance inquiry, credit card limit inquiry, and so on.
- the first multimedia data may include voice data types, video data types, and so on.
- the user can send a call request through the terminal, the computer device obtains the call request, establishes a call connection with the terminal according to the call request, and obtains the first multimedia information about the first service from the terminal through the call connection ⁇ Body data.
- the call connection may include a video connection, a voice connection, and so on. The video connection is used to obtain the video data sent by the terminal connected to the computer device, and the voice connection is used to obtain the voice data sent by the terminal connected to the computer device.
- the first multimedia data includes keywords corresponding to the first service
- the computer device can recognize the first multimedia data, and recognize that the first multimedia data includes keywords corresponding to the first service.
- the keyword is used as the first business attribute information.
- the first multimedia data may be "I want to apply for a credit card”
- the recognized keywords include “transaction” and "credit card”
- the first attribute information includes “transaction” and "credit card”.
- S103 Determine a recognition engine matching the first service attribute information from the shared recognition engine set as the target recognition engine.
- the recognition engine is used to recognize multimedia data.
- the shared recognition engine set includes at least one recognition engine, and the shared recognition engine set may include a recognition engine that recognizes multimedia data corresponding to multiple services.
- one service can correspond to multiple recognition engines, for example, it can include a voice data recognition engine, a text data recognition engine, a facial data recognition engine, and so on.
- One recognition engine can also recognize multiple services.
- the recognition engine matching the first service attribute information refers to the recognition engine that can recognize the multimedia data corresponding to the first service.
- the recognition engine that matches the first business attribute information refers to the recognition engine that can identify the multimedia data corresponding to "credit card processing”
- the recognition engine can recognize text information, voice data, and so on that users fill in for credit card transactions.
- the target recognition engine is a recognition engine that can recognize the voice data and text data.
- the first service attribute information may include at least one of the service level of the first service or the service income of the first service.
- the recognition engine corresponding to the first service attribute information can be determined according to the first service attribute information.
- the service level of the first service refers to the level of identification data that needs to be obtained to process the first service, and the identification data may include at least one of voice data, fingerprint data, and facial data.
- the recognition level of facial data is greater than that of fingerprint data
- the recognition level of fingerprint data is greater than that of voice data, and so on.
- the lower the recognition level of the recognition data the lower the recognition complexity, and the higher the recognition level of the recognition data, the higher the recognition complexity.
- the service level of the first service is lower; if the first service attribute information includes facial data, the service level of the first service is higher.
- a recognition engine with a lower cost can be used to realize the recognition of multimedia data, and the recognition result meets the recognition requirement of service processing.
- the recognition engine with higher recognition accuracy can be used for recognition to improve the recognition accuracy.
- using a recognition engine with higher recognition accuracy can improve the accuracy of recognition; when the service level of the first service is low, using a recognition engine with a lower recognition cost can save services The cost of processing.
- the business revenue of the first business can be the expected revenue of the first business.
- the lower the cost of the recognition engine the higher the business revenue of the first business; the lower the cost of the recognition engine, the higher the cost of the first business.
- S104 Output prompt information about processing the first service.
- the prompt information of the first service refers to process information for processing the first service.
- the prompt information for the first business may include "please fill in the currently displayed identity information", "please aim your face at the camera", "please Blink”, "Please move your face left and right” and so on.
- the user can make corresponding responses based on the prompt information, such as filling in identity information, aligning the face to the camera, etc., so that the terminal can collect the user to respond according to the prompt information of the first service, and get the first service.
- the second multimedia data may include a voice data type, a video data type, and so on.
- the terminal records the voice replied by the user according to the prompt information of the first service to obtain the voice data, that is, the second multimedia data; if the second multimedia data is Video data type, the terminal records the video replied by the user according to the prompt information of the first service to obtain the video data, that is, the second multimedia data.
- S105 Acquire second multimedia data sent for the prompt information from the terminal.
- the terminal since the terminal collects the second multimedia data that the user replies according to the prompt information of the first service in the above steps, the terminal can send the second multimedia data to the computer device, and the computer device obtains the prompt information The second multimedia data sent.
- S106 Send the second multimedia data to the first service platform, so that the first service platform uses the target recognition engine to recognize the second multimedia data and process the first service.
- the first service platform may refer to a platform that processes the first service. For example, if the first business is credit card processing, the first business platform is a banking platform. After the computer device sends the second multimedia data to the first service platform, the first service platform uses the target recognition engine to recognize the second multimedia data and process the first service.
- the first service platform may use the target recognition engine to recognize the second multimedia data and recognize the authenticity of the second multimedia data, and if the second multimedia data has authenticity, process the first service; if If the second multimedia data does not have authenticity, the processing of the first service is ended.
- the target recognition engine is used to recognize the second multimedia data
- the authenticity of the second multimedia data may include: recognizing the second multimedia data Whether the user’s facial information included in the first service platform is the user’s facial information, if so, the second multimedia data is considered authentic; if not, the second multimedia data is considered not authentic .
- the facial information of the user stored by the first service platform may be based on the facial information stored when the user handles historical services on the first service platform. For example, if the user has processed a bank card on the first business platform, the user's facial information stored on the first business platform may be the user's facial information reserved when the user has processed the bank card on the first business platform. If the user does not handle historical business on the first business platform, or the user does not store facial information when handling historical business on the first business platform, the user’s facial information can be obtained from other platforms that store the user’s facial information, for example, from Obtain the user's facial information from the corresponding platforms of the Ministry of Public Security and the Ministry of Civil Affairs.
- the multimedia data sent by the terminal can also be obtained, the second service attribute information is determined by identifying the multimedia data, and the second service attribute information is determined from the shared recognition engine set.
- the recognition engine as the second recognition engine, outputs prompt information about processing the second service; acquires multimedia data sent by the prompt information for the second service from the terminal, and sends the multimedia data to the second service platform for processing The second business.
- the shared recognition engine set includes at least one recognition engine, and different recognition engines correspond to different services, this method can concentrate multiple recognition engines in a set, so that different services share one recognition engine. Engine, there is no need to customize the recognition engine for different businesses, saving costs.
- This method also brings together multiple services to facilitate quick docking to the service platform. Even if users need to handle multiple different services, by identifying the service attribute information in the multimedia data, they can be docked to the corresponding recognition engine and process the corresponding Business, thereby improving the efficiency of business processing.
- the computer equipment in this application can refer to any node equipment in the blockchain.
- the so-called blockchain is a computer technology such as distributed data storage, peer-to-peer transmission (P2P transmission), consensus mechanism, encryption algorithm, etc.
- the new type of application model is essentially a decentralized database; a block chain can be composed of multiple serial transaction records (also called blocks) that are connected and protected by cryptography.
- the connected distributed ledger allows multiple parties to effectively record the transaction, and the transaction can be permanently checked (not tampered with).
- the consensus mechanism refers to the mathematical algorithm that realizes the establishment of trust between different nodes and the acquisition of rights and interests in the blockchain network; that is to say, the consensus mechanism is a mathematical algorithm recognized by all network nodes of the blockchain.
- This application can use the consensus mechanism of the blockchain to realize that multiple services share the recognition engine in the shared recognition engine set, so as to avoid waste of resources and save costs.
- the first service attribute information corresponding to the first service can be obtained.
- the target recognition engine is used for recognition and the first service is processed. Since the shared recognition engine set includes multiple recognition engines, this method can concentrate multiple recognition engines in the shared recognition engine set. Different services can share the recognition engines in the set, and there is no need to customize recognition engines for different services. , Can avoid the waste of resources, save the investment of hardware resources, thereby saving costs.
- the prompt information about processing the first service is output, and the second multimedia data sent for the prompt information is obtained from the terminal. By outputting the prompt information, the terminal can collect the second information obtained by the user according to the prompt information.
- the second multimedia data is sent to the first service platform, so that the first service platform uses the target recognition engine to recognize the second multimedia data and process the first service.
- the user needs to handle the service, he only needs to obtain the service attribute information in the multimedia data, determine the corresponding service and the recognition engine corresponding to the service, and send the multimedia data corresponding to the first service to the first service platform. That is, the corresponding recognition engine can be used to identify and process the first service. It can realize the separation of the two processes of determining the recognition engine and processing the business, and realize the quick connection to the business processing platform for business processing.
- the first service attribute information includes the identifier of the first service
- the above step S104 may include the following steps s11 to s13.
- s11 Determine the processing platform for the first service according to the identifier of the first service.
- the identifier of the first service is used to uniquely indicate the first service.
- the identifier of the first service can be the name of the first service, the abbreviation of the name of the first service, and the pinyin of the name of the first service.
- the processing first business platform is a platform that can process the first business. For example, if the first business identifier is Ping An Bank card processing, the first business platform is the Ping An Bank platform.
- the computer device can obtain the process information for handling the first service from the first service platform, such as obtaining user identity information, obtaining user facial data, etc. in the above steps, to obtain prompt information for the first service , Output the first prompt message to the terminal.
- the user can view the prompt information through the terminal, and make a corresponding reply according to the prompt information to conduct business processing.
- the first multimedia data includes first voice data
- the above step S102 may include the following steps s21 to s23.
- s21 Perform voice recognition on the first voice data to obtain the first keyword associated with the service in the first voice data, and determine the first service attribute information according to the first keyword.
- the first voice data refers to data obtained by collecting the voice of the user.
- the first keyword associated with the business may be, for example, the name of the business, the abbreviation of the business name, and the number used to represent the business, and so on.
- the computer device obtains the first keyword associated with the service in the first voice data by performing voice recognition on the first voice data, such as the name of the service, and then determines the first service attribute information according to the name of the service.
- the first voice data is "I want to apply for a bank card" and the first keyword is "bank card”.
- the first business attribute information can be determined by obtaining the words before and after the first keyword. For example, it is determined that the first business attribute information includes "Apply for a bank card.”
- the computer device may use ASR technology or other voice recognition technology to recognize voice data, obtain the first keyword associated with the service in the first voice data, and determine the first service attribute information according to the first keyword.
- ASR technology or other voice recognition technology to recognize voice data, obtain the first keyword associated with the service in the first voice data, and determine the first service attribute information according to the first keyword.
- s22 Convert the first voice data to obtain first text data corresponding to the first voice data.
- the voice-type data can be converted into text-type data to obtain the first text data.
- s23 Perform keyword extraction on the first text data to obtain a second keyword associated with the business in the first text data, and determine the first business attribute information according to the second keyword.
- the first keyword and the second keyword may be the same, and the first keyword and the second keyword may also be different.
- the computer device converts the first voice data into the first text data, it performs keyword extraction on the first text data to obtain the second keyword associated with the service in the first text data, and determine the first service according to the second keyword Property information.
- the computer device first performs word segmentation processing on the first text data, and divides the first text data into at least one word segmentation; obtains a stop word set, and the stop word set includes at least one word that is not related to business; Search for a target word that matches the at least one participle in the word set; delete the target word in the at least one participle; perform keyword extraction on at least one participle after deleting the target word to obtain the second keyword, according to the second key The word determines the first business attribute information.
- the first text data is "I want to apply for a bank card”
- the result of word segmentation processing is "I want to apply for a bank card” which is divided into 4 words, and then these 4 words are divided into the stop word set.
- Each stop word is matched. If it matches the 2 participles of "I” and “Want”, delete these 2 participles to obtain “bank card application”, and perform keyword extraction on “bank card application” to get the first
- the second keyword "bank card” the first business attribute information is determined according to the second keyword.
- the cost of voice recognition is lower, so in the case of cost savings, Voice recognition is adopted; or, the accuracy of keyword extraction by converting the voice data into text data is relatively high.
- the voice data is converted into text data for keyword extraction.
- the first service attribute information can be obtained, so that the biological information can be determined according to the first service attribute information.
- the recognition engine and the first service platform can then perform corresponding service processing.
- the first service attribute information includes the service level of the first service
- the above step S103 may include the following steps s31 to s32.
- s31 Acquire the recognition level of the recognition engine in the shared recognition engine set, and the recognition level of the recognition engine is used to reflect the accuracy of the recognition engine in recognizing the multimedia data.
- s32 Determine the recognition engine whose recognition level matches the service level of the first service in the shared recognition engine set as the target recognition engine.
- steps s31 to s32 the higher the recognition level of the recognition engine, the higher the accuracy of the recognition engine in recognizing the multimedia data; the lower the recognition level of the recognition engine, the lower the accuracy of the recognition engine in recognizing the multimedia data.
- the higher the business level of the first service the higher the identification level of the identification data that needs to be obtained to process the first business; the lower the business level of the first business, the higher the identification level of the identification data that needs to be acquired to process the first business Low.
- the recognition data that needs to be obtained to process the first service is voice data, which means that the service level of the first service is low, and the recognition level of the recognition engine is low; the recognition level of the recognition engine is low;
- the recognition data is facial data, which indicates that the service level of the first service is higher, and the recognition level of the recognition engine is higher than that of the service level matching degree of the first service.
- the service level of the first service may be determined according to the type of the identification data. For example, when the identification data that needs to be acquired to process the first service includes voice data, fingerprint data, and facial data, the business level of the first business is higher; when the identification data that needs to be acquired to process the first business includes voice data and fingerprint data, then The business level of the first business is lower.
- the identification data needed to process the first service 1 to the first service 4 includes identification data 1 to identification data 4, and the identification data 1 includes voice data and fingerprint data, and the identification data 2 includes voice data and facial data, and identification data.
- the business level of the first business 1 is less than the business level of the first business 2
- the business level of the first business 2 is less than that of the first business 3.
- the service level of the first service 3 is less than the service level of the first service 4.
- the recognition engine in the shared recognition engine set whose recognition level matches the business level of the first service is determined as the target recognition engine.
- the service level of the first service is low, a recognition engine with a lower recognition level can be used to save costs; when the service level of the first service is higher, a recognition engine with a higher recognition level can be used , Which can improve the accuracy of recognizing multimedia data.
- the first business attribute information includes the business income of the first business
- the above step S103 may include the following steps s41 to s42.
- s41 Obtain the recognition cost of the recognition engine in the shared recognition engine set.
- s42 Determine the recognition engine whose recognition cost matches the business income of the first business in the shared recognition engine set as the target recognition engine.
- the business income of the first business may be the expected income of the first business.
- the identification cost of the recognition engine refers to the amount of currency required to purchase or use the recognition engine. The more the recognition cost of the recognition engine is Lower, the higher the business income of the first business; the higher the recognition cost of the recognition engine, the lower the business income of the first business.
- the computer device obtains the recognition cost of the recognition engine in the shared recognition engine set, and determines the recognition engine that matches the recognition cost of the first business in the shared recognition engine set as the target recognition engine. In the case that the business income of the first service is high, the recognition engine with lower identification cost is used to identify the multimedia data, which can reduce the identification cost, thereby increasing the business income of the first service.
- FIG. 2 is a schematic flowchart of a data processing method provided in an embodiment of the present application.
- the method is applied to computer equipment; as shown in Figure 2, the method includes the following steps.
- S201 Acquire first multimedia data about a first service from a terminal.
- S203 Determine a recognition engine matching the first service attribute information from the shared recognition engine set as the target recognition engine.
- S205 Acquire the second multimedia data sent for the prompt information from the terminal.
- the second multimedia data includes the first video data and the second voice data.
- steps S201 to S205 reference may be made to the description of steps S101 to S105 in the embodiment corresponding to FIG. 1, which will not be repeated here.
- S206 Acquire a first image of a user corresponding to the terminal according to the first video data.
- the first video data is the video data collected by the terminal and obtained by the user responding according to the prompt information for processing the first service.
- the first video data includes the user's facial image.
- the computer device may intercept the first video data every preset time to obtain the first image containing the user's face, and obtain the first image of the user corresponding to the terminal. For example, the image in the first video data may be intercepted every 0.5 seconds to obtain the first image. For example, if the duration of the first video data is 2 seconds, the number of first images of the user acquired is 4.
- S207 Send the first image, the first video data, and the second voice data to the first service platform, so that the first service platform verifies the legitimacy of the terminal according to the first image, and uses the target recognition engine when the terminal has legitimacy Recognize the first video data and the second voice data, and process the first service.
- the second voice data is the voice data collected by the terminal and the user responds according to the prompt information for processing the first service.
- the computer device sends the first image, the first video data, and the second voice data to the first service platform so that the first service platform verifies the legitimacy of the terminal according to the first image.
- the target recognition engine is used to The first video data and the second voice data are recognized, and the first service is processed.
- the first service platform after the first service platform obtains the first image, the first video data, and the second voice data, it can use the target recognition engine to recognize the first image, and determine the user’s facial image in the first image and the first service Whether the user image stored on the platform is the facial image of the same user, if it is, it is determined that the terminal is legal, and the target recognition engine is used to identify the first video data and the second voice data, and process the first service. If not, it is determined that the terminal does not have legitimacy, and warning information indicating that the terminal does not have legitimacy is generated, so that the user can adjust the posture according to the warning information.
- the first service platform uses the target recognition engine to recognize the first video data and the second voice data, it can obtain the third image of the user corresponding to the second voice data in the first video data , That is, the third image when the user answers the question according to the prompt information of the first service is obtained from the first video data, and the third image contains the facial image of the user.
- the authenticity of the question answered by the user is determined according to the micro-expression when the user answers the question. If it is determined through micro-expression recognition that the authenticity of the question answered by the user is high, the first service is processed.
- the instruction information used to verify the user's identity for the second time is sent or the question with the abnormal micro-expression of the user is output again. If the second verification is passed or the facial expression when the user answers the question again indicates that the authenticity of the question answered by the user is high, the first service is processed. If the second verification fails or the user’s facial expression when answering the question again indicates that the authenticity of the question answered by the user is low, the output is used to instruct the user to conduct business processing at the manual business processing office corresponding to the first business platform, and end the processing of the first business platform.
- the authenticity of the user’s identity can be improved, and the first service platform can perform verification on the third image in the first video data.
- Micro-expression recognition can identify the authenticity of the question answered by the user, thereby realizing the second verification of the user's identity information and improving the accuracy of business processing.
- the above step method may include the following steps s51 to s54.
- s53 Acquire a second image of the user according to the third video data.
- the computer device obtains the warning information sent by the first service platform to indicate that the terminal is not legal, it outputs adjustment information for instructing the user to adjust the posture, so that the user can follow the adjustment information Perform posture adjustment. For example, when the user’s face is not aligned with the camera of the terminal, the adjusted user’s face is aligned with the camera of the terminal; or, when the camera of the terminal includes user A and user B, and user A is required For the user who handles the first service, only user A is included in the camera of the adjusted terminal.
- the computer device obtains the third multimedia data sent by the terminal for the adjustment information, the third multimedia data includes third video data; obtains the user's second image according to the third video data; sends the second image to the first service platform , So that the first service platform verifies the legitimacy of the terminal based on the second image.
- the second image includes the facial image of the user. If the second image and the facial image of the user stored in the first service platform are the facial image of the same user, the terminal has legitimacy and processes the first service. If the second image and the user's facial image stored in the first service platform are not the same user's facial image, the terminal does not have legitimacy, and the processing of the first service is terminated, and the output is used to instruct the user to correspond to the manual on the first service platform.
- the business handling office conducts business handling and ends the processing of the first business.
- the user is prompted to adjust the posture by outputting adjustment information, thereby verifying the legitimacy of the terminal, thereby improving the authenticity of the user identity information verification.
- FIG. 3 is a schematic diagram of the composition structure of a data processing device provided by an embodiment of the present application.
- the above data processing device may be a computer program (including program code) running in a computer device.
- the data processing device is An application software; the device can be used to execute the corresponding steps in the method provided in the embodiments of this application.
- the device 30 includes: a first obtaining module 301, which is used to obtain first multimedia data about a first service from a terminal; and a data recognition module 302, which is used to recognize the first multimedia data to obtain the first multimedia data.
- a service attribute information includes at least one of the service level of the first service or the service income of the first service;
- the engine determining module 303 is configured to determine the first service from the set of shared recognition engines
- a recognition engine with matching service attribute information is used as a target recognition engine;
- an information output module 304 is used to output prompt information about processing the first service;
- a second acquisition module 305 is used to obtain information specific to the prompt information from the terminal The second multimedia data sent;
- the service processing module 306, configured to send the second multimedia data to the first service platform, so that the first service platform uses the target recognition engine for the second multimedia The data is identified and the first service is processed.
- the information output module 304 is configured to: determine to process the first service platform according to the identifier of the first service; obtain prompt information about processing the first service from the first service platform; and output the first service platform; Prompt information.
- the first multimedia data includes first voice data
- the data recognition module 302 is specifically configured to: perform voice recognition on the first voice data to obtain the first voice data associated with the service in the first voice data.
- a keyword the first service attribute information is determined according to the first keyword; or, the first voice data is converted to obtain the first text data corresponding to the first voice data; the first text data is keyed Word extraction is used to obtain the second keyword associated with the business in the first text data; the first business attribute information is determined according to the second keyword.
- the first service attribute information includes the service level of the first service
- the engine determining module 303 is specifically configured to: obtain the recognition level of the recognition engine in the shared recognition engine set, and the recognition level of the recognition engine is used To reflect the accuracy of the recognition engine in recognizing the multimedia data; the recognition engine whose recognition level matches the service level of the first service in the shared recognition engine set is determined as the target recognition engine.
- the first service attribute information includes the business income of the first service; the engine determining module 303 is specifically configured to: obtain the recognition cost of the recognition engines in the shared recognition engine set; and in the shared recognition engine set The identification engine whose identification cost matches the business income of the first business is determined as the target identification engine.
- the second multimedia data includes first video data and second voice data
- the service processing module 306 is specifically configured to: obtain the first image of the user corresponding to the terminal according to the first video data; The first image, the first video data, and the second voice data are sent to the first service platform, so that the first service platform verifies the legitimacy of the terminal according to the first image, and when the terminal has legitimacy , Using the target recognition engine to recognize the first video data and the second voice data, and process the first service.
- the device further includes: an adjustment module 307, configured to: if the warning information sent by the first service platform indicating that the terminal is not legal is obtained, outputting an instruction to instruct the user to adjust the posture Adjustment information; obtaining third multimedia data sent by the terminal for the adjustment information, where the third multimedia data includes third video data; obtaining a second image of the user according to the third video data; The image is sent to the first service platform, so that the first service platform verifies the legitimacy of the terminal according to the second image.
- an adjustment module 307 configured to: if the warning information sent by the first service platform indicating that the terminal is not legal is obtained, outputting an instruction to instruct the user to adjust the posture Adjustment information; obtaining third multimedia data sent by the terminal for the adjustment information, where the third multimedia data includes third video data; obtaining a second image of the user according to the third video data; The image is sent to the first service platform, so that the first service platform verifies the legitimacy of the terminal according to the second image.
- the first service attribute information corresponding to the first service can be obtained.
- the target recognition engine is used for recognition and the first service is processed. Since the shared recognition engine set includes multiple recognition engines, this method can concentrate multiple recognition engines in the shared recognition engine set. Different services can share the recognition engines in the set, and there is no need to customize recognition engines for different services. , Can avoid the waste of resources, save the investment of hardware resources, thereby saving costs.
- the prompt information about processing the first service is output, and the second multimedia data sent for the prompt information is obtained from the terminal. By outputting the prompt information, the terminal can collect the second information obtained by the user according to the prompt information.
- the second multimedia data is sent to the first service platform, so that the first service platform uses the target recognition engine to recognize the second multimedia data and process the first service.
- the user needs to handle the service, he only needs to obtain the service attribute information in the multimedia data, determine the corresponding service and the recognition engine corresponding to the service, and send the multimedia data corresponding to the first service to the first service platform. That is, the corresponding recognition engine can be used to identify and process the first service. It can realize the separation of the two processes of determining the recognition engine and processing the business, and realize the quick connection to the business processing platform for business processing.
- FIG. 4 is a schematic diagram of the composition structure of a computer device provided by an embodiment of the present application.
- the foregoing computer device 40 may include: a processor 401, a network interface 404, and a memory 405.
- the foregoing computer device 40 may also include: a user interface 403, and at least one communication bus 402.
- the communication bus 402 is used to implement connection and communication between these components.
- the user interface 403 may include a display screen (Display) and a keyboard (Keyboard), and the optional user interface 403 may also include a standard wired interface and a wireless interface.
- the network interface 404 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).
- the memory 405 may be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), for example, at least one magnetic disk memory.
- the memory 405 may also be at least one storage device located far away from the foregoing processor 401.
- the memory 405 as a computer-readable storage medium may include an operating system, a network communication module, a user interface module, and a device control application program.
- the network interface 404 can provide network communication functions; the user interface 403 is mainly used to provide an input interface for the user; and the processor 401 can be used to call the device control application stored in the memory 405 Program to realize: obtain first multimedia data about the first service from the terminal; identify the first multimedia data to obtain the first service attribute information, and the first service attribute information includes the first At least one of the business level of the service or the business income of the first service; the recognition engine matching the attribute information of the first service is determined from the shared recognition engine set as the target recognition engine; output information about processing the first service Prompt information; obtain the second multimedia data sent for the prompt information from the terminal; send the second multimedia data to the first service platform, so that the first service platform uses the target recognition engine to The second multimedia data is identified, and the first service is processed.
- the computer device 40 described in the embodiment of the present application can perform the foregoing data processing method described in the foregoing embodiment corresponding to FIG. 1 and FIG. 2, and may also perform the foregoing data processing method in the foregoing embodiment corresponding to FIG. 3
- the description of the device will not be repeated here.
- the description of the beneficial effects of using the same method will not be repeated.
- the first service attribute information corresponding to the first service can be obtained.
- the target recognition engine is used for recognition and the first service is processed. Since the shared recognition engine set includes multiple recognition engines, this method can concentrate multiple recognition engines in the shared recognition engine set. Different services can share the recognition engines in the set, and there is no need to customize recognition engines for different services. , Can avoid the waste of resources, save the investment of hardware resources, thereby saving costs.
- the prompt information about processing the first service is output, and the second multimedia data sent for the prompt information is obtained from the terminal. By outputting the prompt information, the terminal can collect the second information obtained by the user according to the prompt information.
- the second multimedia data is sent to the first service platform, so that the first service platform uses the target recognition engine to recognize the second multimedia data and process the first service.
- the user needs to handle the service, he only needs to obtain the service attribute information in the multimedia data, determine the corresponding service and the recognition engine corresponding to the service, and send the multimedia data corresponding to the first service to the first service platform. That is, the corresponding recognition engine can be used to identify and process the first service. It can realize the separation of the two processes of determining the recognition engine and processing the business, and realize the quick connection to the business processing platform for business processing.
- the embodiment of the present application also provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and the computer program includes program instructions that, when executed by a computer, cause the computer to execute Method, the computer can be a part of the aforementioned computer equipment.
- the aforementioned processor 401 For example, the aforementioned processor 401.
- the storage medium involved in this application such as a computer-readable storage medium, may be non-volatile or volatile.
- the program instructions may be deployed and executed on one computer device, or be deployed on multiple computer devices located in one location, or on multiple computer devices that are distributed in multiple locations and interconnected by a communication network
- Execution, multiple computer devices distributed in multiple locations and interconnected through a communication network can form a blockchain network.
- the program can be stored in a computer readable storage medium. At this time, it may include the procedures of the embodiments of the above-mentioned methods.
- the storage medium can be a magnetic disk, an optical disc, a read-only memory (Read-Only Memory, ROM) or a random access memory (Random Access Memory, RAM) etc.
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Abstract
本申请实施例公开了一种数据处理方法、装置、设备及介质,涉及人工智能中的语音处理技术,且可应用于区块链网络,其中,方法包括:从终端中获取关于第一业务的第一多媒体数据;对第一多媒体数据进行识别,得到第一业务属性信息;从共享识别引擎集合中确定与第一业务属性信息匹配的识别引擎,作为目标识别引擎;输出关于处理第一业务的提示信息;从终端中获取针对提示信息所发送的第二多媒体数据;将第二多媒体数据发送至第一业务平台,以使第一业务平台采用目标识别引擎对第二多媒体数据进行识别,处理第一业务。采用本申请实施例,可避免资源浪费,降低成本。
Description
本申请要求于2020年9月8日提交中国专利局、申请号为202010918464.6,发明名称为“一种数据处理方法、装置、设备及介质”的中国专利申请的优先权,其全部内容通过引用结合在本申请中。
本申请涉及人工智能中的语音处理技术,尤其涉及一种数据处理方法、装置、设备及介质。
目前在很多行业内都会使用到视频机器人通话,例如服务行业中的业务咨询、业务办理等,视频机器人通话已经开始逐渐取代人工,且可以实现随时随地的业务办理。发明人意识到,在用户呼叫视频机器人时,通常是根据用户需要办理的业务的不同来对接不同的识别引擎,通过识别引擎进行业务处理。由于不同的业务需要由不同的服务器来处理,视频机器人需要承载较多的业务属性才能实现对接不同的识别引擎,每种业务均需要定制开发不同的识别引擎,浪费大量资源,且成本较高。
本申请实施例提供一种数据处理方法、装置、设备及介质,可避免资源浪费,降低成本。
本申请实施例一方面提供一种数据处理方法,包括:从终端中获取关于第一业务的第一多媒体数据;对该第一多媒体数据进行识别,得到该第一业务属性信息,该第一业务属性信息包括该第一业务的业务等级或者该第一业务的业务收益中的至少一种;从共享识别引擎集合中确定与该第一业务属性信息匹配的识别引擎,作为目标识别引擎;输出关于处理该第一业务的提示信息;从该终端中获取针对该提示信息所发送的第二多媒体数据;将该第二多媒体数据发送至第一业务平台,以使该第一业务平台采用该目标识别引擎对该第二多媒体数据进行识别,处理该第一业务。
本申请实施例一方面提供一种数据处理装置,包括:第一获取模块,用于从终端中获取关于第一业务的第一多媒体数据;数据识别模块,用于对该第一多媒体数据进行识别,得到该第一业务属性信息,该第一业务属性信息包括该第一业务的业务等级或者该第一业务的业务收益中的至少一种;引擎确定模块,用于从共享识别引擎集合中确定与该第一业务属性信息匹配的识别引擎,作为目标识别引擎;信息输出模块,用于输出关于处理该第一业务的提示信息;第二获取模块,用于从该终端中获取针对该提示信息所发送的第二多媒体数据;业务处理模块,用于将该第二多媒体数据发送至第一业务平台,以使该第一业务平台采用该目标识别引擎对该第二多媒体数据进行识别,处理该第一业务。
本申请一方面提供了一种计算机设备,包括:处理器、存储器、网络接口;上述处理器与存储器、网络接口相连,其中,网络接口用于提供数据通信功能,上述存储器用于存储计算机程序,上述处理器用于调用上述计算机程序,以执行以下方法:从终端中获取关于第一业务的第一多媒体数据;对该第一多媒体数据进行识别,得到该第一业务属性信息,该第一业务属性信息包括该第一业务的业务等级或者该第一业务的业务收益中的至少一种;从共享识别引擎集合中确定与该第一业务属性信息匹配的识别引擎,作为目标识别引擎;输出关于处理该第一业务的提示信息;从该终端中获取针对该提示信息所发送的第二多媒体数据;将该第二多媒体数据发送至第一业务平台,以使该第一业务平台采用该目标识别引擎对该第二多媒体数据进行识别,处理该第一业务。
本申请实施例一方面提供了一种计算机可读存储介质,该计算机可读存储介质存储有计算机程序,该计算机程序包括程序指令,该程序指令当被处理器执行时使该处理器执行以下方法:从终端中获取关于第一业务的第一多媒体数据;对该第一多媒体数据进行识别,得到该第一业务属性信息,该第一业务属性信息包括该第一业务的业务等级或者该第一业务的业务收益中的至少一种;从共享识别引擎集合中确定与该第一业务属性信息匹配的识别引擎,作为目标识别引擎;输出关于处理该第一业务的提示信息;从该终端中获取针对该提示信息所发送的第二多媒体数据;将该第二多媒体数据发送至第一业务平台,以使该第一业务平台采用该目标识别引擎对该第二多媒体数据进行识别,处理该第一业务。
本申请实施例可以避免资源浪费,节省硬件资源的投入,从而节省成本。进一步的,可以实现将确定识别引擎和处理业务两种流程进行分离,实现快速对接到业务处理平台进行业务处理。
为了更清楚地说明本申请实施例中的技术方案,下面将对实施例中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本申请的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。
图1是本申请实施例提供的一种数据处理方法的流程示意图。
图2是本申请实施例提供的一种数据处理方法的流程示意图。
图3是本申请实施例提供的一种数据处理装置的组成结构示意图。
图4是本申请实施例提供的一种计算机设备的组成结构示意图。
下面将结合本申请实施例中的附图,对本申请实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅是本申请一部分实施例,而不是全部的实施例。基于本申请中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本申请保护的范围。
本申请的技术方案可应用于人工智能、区块链和/或大数据技术领域,以实现业务处理。
人工智能技术是一门综合学科,涉及领域广泛,既有硬件层面的技术也有软件层面的技术。人工智能基础技术一般包括如传感器、专用人工智能芯片、云计算、分布式存储、大数据处理技术、操作/交互***、机电一体化等技术。人工智能软件技术主要包括计算机视觉技术、语音处理技术、自然语言处理技术以及机器学习/深度学习等几大方向。
其中,语音处理技术(Speech
Technology)的关键技术有自动语音识别技术(ASR)和语音合成技术(TTS)以及声纹识别技术。让计算机能听、能看、能说、能感觉,是未来人机交互的发展方向,其中语音成为未来最被看好的人机交互方式之一。
本申请涉及人工智能中的语音处理技术,利用语音处理技术对关于第一业务的第一多媒体数据进行识别,得到第一业务属性信息,从共享识别引擎集合中确定与第一业务属性信息匹配的目标识别引擎,将第二多媒体数据发送至第一业务平台,以使第一业务平台使用目标识别引擎对第二多媒体数据进行识别,处理第一业务。由于本申请中不同业务均可共享该集合中的识别引擎,不需要为不同业务定制识别引擎,可以避免资源浪费,节省硬件资源的投入,从而节省成本。本申请可适用于智慧政务、智慧教育等领域,有利于推动智慧城市的建设。
本申请的技术方案适用于对终端发送的多媒体数据进行识别,从而根据多媒体数据中的业务属性信息进行相应的业务处理的场景中。例如本申请的技术方案适用于远程面审、视频回访、远程开户等场景中,通过从终端中获取关于第一业务的第一多媒体数据,对第一多媒体数据进行识别,得到第一业务的属性信息,根据属性信息确定出与属性信息匹配的目标识别引擎,输出关于处理第一业务的提示信息,以使终端根据该提示信息发送第二多媒体数据,通过将第二多媒体数据发送至第一业务对应的业务平台,以使业务平台采用目标识别引擎识别第二多媒体数据,处理第一业务。通过对包含业务的多媒体数据进行识别,可以确定多媒体数据中的业务属性信息,从而根据业务属性信息办理相应业务。
请参见图1,图1是本申请实施例提供的一种数据处理方法的流程示意图,该方法可以应用于计算机设备,其中,计算机设备包括手机、平板电脑、笔记本电脑、掌上电脑、智能音响、移动互联网设备(MID,mobile
internet device)、POS(Point Of
Sales,销售点)机、可穿戴设备(例如智能手表、智能手环等)等;还可以是指是一***立的服务器、或由若干台服务器组成的服务器集群、或云计算中心。如图1所示,该方法包括以下步骤。
S101,从终端中获取关于第一业务的第一多媒体数据。
这里,终端可以是指用户用于进行业务处理的终端。终端可以包括手机、平板电脑、笔记本电脑、掌上电脑、智能音响、移动互联网设备(MID,mobile
internet device)、POS(Point Of
Sales,销售点)机、可穿戴设备(例如智能手表、智能手环等)等。第一业务可以包括用户需要办理的业务,例如购买XX产险、银行贷款、银行卡办理、***办理,等等。或者,第一业务也可以包括用户需要的服务,例如银行卡余额查询、***额度查询,等等。第一多媒体数据可以包括语音数据类型、视频数据类型,等等。
具体实现中,用户可以通过终端发送呼叫请求,计算机设备获取到该呼叫请求,根据该呼叫请求建立与终端之间的通话连接,通过该通话连接从终端中获取关于第一业务的第一多媒体数据。这里,通话连接可以包括视频连接、语音连接,等等。视频连接用于获取与计算机设备连接的终端发送的视频数据、语音连接用于获取与计算机设备连接的终端发送的语音数据。
S102,对第一多媒体数据进行识别,得到第一业务属性信息。
这里,第一多媒体数据中包含与第一业务对应的关键词,计算机设备可以对第一多媒体数据进行识别,识别到第一多媒体数据中包含与第一业务对应的关键词,则将该关键词作为第一业务属性信息。例如,第一多媒体数据例如可以为“我要办理***”,则识别到的关键词包括“办理”、“***”,则第一属性信息包括“办理”、“***”。
S103,从共享识别引擎集合中确定与第一业务属性信息匹配的识别引擎,作为目标识别引擎。
这里,识别引擎用于对多媒体数据进行识别。共享识别引擎集合中包括至少一个识别引擎,共享识别引擎集合中可以包括识别多种业务对应的多媒体数据的识别引擎。其中,一种业务可以对应多种识别引擎,例如可以包含语音数据识别引擎、文本数据识别引擎、面部数据识别引擎,等等。一种识别引擎也可以识别多种业务。与第一业务属性信息匹配的识别引擎是指可以识别第一业务对应的多媒体数据的识别引擎。例如,第一业务属性信息为“办理***”,则第一业务可以为“办理***”,则与第一业务属性信息匹配的识别引擎是指可以识别“办理***”对应的多媒体数据的识别引擎,也就是说,识别引擎可以识别用户办理***填写的文本信息、语音数据,等等。例如,用户需要办理第一业务时,用户通过终端发送办理第一业务所需的语音数据以及文本数据至计算机设备,则目标识别引擎为可以对该语音数据以及文本数据进行识别的识别引擎。
可选的,第一业务属性信息可以包括第一业务的业务等级或者第一业务的业务收益中的至少一种。则可以根据第一业务属性信息确定与第一业务属性信息对应的识别引擎。第一业务的业务等级是指处理第一业务需要获取的识别数据的等级,识别数据可以包括语音数据、指纹数据、面部数据中的至少一种。例如,面部数据的识别等级大于指纹数据的识别等级,指纹数据的识别等级大于语音数据的识别等级,等等。识别数据的识别等级越低表示识别复杂程度越低,识别数据的识别等级越高表示识别复杂程度越高。也就是说,若第一业务属性信息只包括语音数据,则第一业务的业务等级较低;若第一业务属性信息包括面部数据,则第一业务的业务等级较高。第一业务的业务等级较低时,可以使用成本较低的识别引擎即可实现对多媒体数据的识别,且识别结果满足业务处理的识别需求。第一业务的业务等级较高时,可以使用识别准确度较高的识别引擎进行识别,提高识别精度。当第一业务的业务等级较高时,使用识别精度较高的识别引擎,可以提高识别的准确度;当第一业务的业务等级较低时,使用识别成本较低的识别引擎,可以节省业务处理的成本。
第一业务的业务收益可以为对第一业务的预期收益,例如,识别引擎对应的成本越低,则第一业务的业务收益越高;识别引擎对应的成本越低高,则第一业务的业务收益越低。
S104,输出关于处理第一业务的提示信息。
这里,第一业务的提示信息是指处理第一业务的流程信息。例如,处理第一业务的流程信息包括获取用户身份信息、获取用户面部数据,则第一业务的提示信息可以包括“请填写当前显示的身份信息”、“请将面部对准摄像头”、“请眨眨眼”、“请左右移动面部”等等。通过输出关于处理第一业务的提示信息,用户可以根据该提示信息进行相应的回复,例如填写身份信息、面部对准摄像头等,以使终端采集用户根据第一业务的提示信息进行回复,得到第二多媒体数据。这里,第二多媒体数据可以包括语音数据类型、视频数据类型,等等。若第二多媒体数据为语音数据类型,则终端对用户根据第一业务的提示信息所回复的语音进行录音,得到语音数据,即第二多媒体数据;若第二多媒体数据为视频数据类型,则终端对用户根据第一业务的提示信息所回复的视频进行录制,得到视频数据,即第二多媒体数据。
S105,从终端中获取针对提示信息所发送的第二多媒体数据。
这里,由于上述步骤中终端采集用户根据第一业务的提示信息所回复的第二多媒体数据,因此,终端可以将第二多媒体数据发送至计算机设备,则计算机设备获取到针对提示信息所发送的第二多媒体数据。
S106,将第二多媒体数据发送至第一业务平台,以使第一业务平台采用目标识别引擎对第二多媒体数据进行识别,处理第一业务。
这里,第一业务平台可以是指处理第一业务的平台。例如,第一业务为办理***,则第一业务平台为银行平台。计算机设备将第二多媒体数据发送至第一业务平台后,第一业务平台采用目标识别引擎对第二多媒体数据进行识别,处理第一业务。
具体的,第一业务平台可以采用目标识别引擎对第二多媒体数据进行识别,识别第二多媒体数据的真实性,若第二多媒体数据具有真实性,处理第一业务;若第二多媒体数据不具有真实性,则结束处理第一业务。例如,第二多媒体数据中包括用户的面部信息,则采用目标识别引擎对第二多媒体数据进行识别,识别第二多媒体数据的真实性可以包括:识别第二多媒体数据中包括的用户的面部信息是否为第一业务平台存储的该用户的面部信息,若是,则认为第二多媒体数据具有真实性;若否,则认为第二多媒体数据不具有真实性。其中,第一业务平台存储的用户的面部信息可以根据该用户在该第一业务平台办理的历史业务时存储的面部信息。例如,用户曾在该第一业务平台办理了银行卡,则第一业务平台存储的用户的面部信息可以为用户曾在该第一业务平台办理该银行卡时预留的用户面部信息。若用户在该第一业务平台未办理历史业务,或者用户在第一业务平台办理历史业务时未存储面部信息,则可以从其他存储有用户面部信息的平台中获取用户的面部信息,例如可以从公安部、民政部等机构对应的平台中获取用户的面部信息。
可选的,在处理第一业务后,还可以获取终端发送的多媒体数据,通过对该多媒体数据进行识别确定第二业务属性信息,并从共享识别引擎集合中确定与第二业务属性信息匹配的识别引擎,作为第二识别引擎,输出关于处理第二业务的提示信息;从终端中获取针对第二业务的提示信息所发送的多媒体数据,并将该多媒体数据发送至第二业务平台,以处理第二业务。也就是说,由于共享识别引擎集合包括至少一个识别引擎,且不同的识别引擎与不同的业务对应,因此,该种方式可以将多个识别引擎集中在一个集合中,实现不同业务均共享一个识别引擎,不需要为不同业务定制识别引擎,节省成本。该种方式也将多种业务集中在一起,便于快速对接到业务平台,即使用户需要办理多种不同业务,通过识别多媒体数据中的业务属性信息,即可对接到对应的识别引擎,处理对应的业务,从而提高业务处理效率。
可选的,本申请中的计算机设备可以是指区块链中的任一节点设备,所谓区块链是一种分布式数据存储、点对点传输(P2P传输)、共识机制、加密算法等计算机技术的新型应用模式,其本质上是一个去中心化的数据库;区块链可由多个借由密码学串接并保护内容的串连交易记录(又称区块)构成,用区块链所串接的分布式账本能让多方有效纪录交易,且可永久查验此交易(不可篡改)。其中,共识机制是指区块链网络中实现不同节点之间建立信任、获取权益的数学算法;也就是说,共识机制是区块链各网络节点共同认可的一种数学算法。本申请可利用区块链的共识机制,来实现多种业务共用共享识别引擎集合中的识别引擎,避免资源浪费,节省成本。
本申请实施例中,通过对第一多媒体数据进行识别,可以获取到与第一业务对应的第一业务属性信息。通过确定第一业务对应的目标识别引擎,在后续处理第一业务时,使用该目标识别引擎进行识别,处理第一业务。由于共享识别引擎集合中包括多个识别引擎,即该种方式可以将多个识别引擎集中在共享识别引擎集合中,不同业务均可共享该集合中的识别引擎,不需要为不同业务定制识别引擎,可以避免资源浪费,节省硬件资源的投入,从而节省成本。进一步的,输出关于处理第一业务的提示信息,从终端中获取针对提示信息所发送的第二多媒体数据,通过输出提示信息,终端可以采集用户根据该提示信息进行回复得到的第二多媒体数据。将第二多媒体数据发送至第一业务平台,以使第一业务平台采用目标识别引擎对第二多媒体数据进行识别,处理第一业务。在用户需要进行业务办理时,只需要通过获取多媒体数据中的业务属性信息,确定对应的业务以及业务对应的识别引擎,将第一业务对应的多媒体数据发送至第一业务平台,第一业务平台即可采用对应的识别引擎进行识别,处理第一业务。可以实现将确定识别引擎和处理业务两种流程进行分离,实现快速对接到业务处理平台进行业务处理。
在一个实施例中,该第一业务属性信息包括第一业务的标识,上述步骤S104中可包括如下步骤s11~s13。
s11,根据第一业务的标识确定处理第一业务平台。
s12,从第一业务平台中获取关于处理第一业务的提示信息。
s13,输出第一提示信息。
在步骤s11~s13中,第一业务的标识用于唯一的指示该第一业务,例如第一业务的标识可以为第一业务的名称、第一业务的名称简写、第一业务的名称的拼音、第一业务的名称的拼音的缩写、以及用于指示第一业务的编号,等等。则处理第一业务平台为可以对第一业务进行处理的平台,例如第一业务标识为平安银行卡办理,则第一业务平台为平安银行平台。计算机设备通过确定第一业务平台,可以从第一业务平台中获取办理第一业务的流程信息,例如上述步骤中的获取用户身份信息、获取用户面部数据,等等,得到第一业务的提示信息,输出第一提示信息至终端。用户可以通过终端查看到该提示信息,并根据提示信息进行对应的回复,以进行业务办理。
在一个实施例中,该第一多媒体数据包括第一语音数据,上述步骤S102中可包括如下步骤s21~s23。
s21,对第一语音数据进行语音识别,得到第一语音数据中与业务相关联的第一关键词,根据第一关键词确定第一业务属性信息。
这里,第一语音数据是指通过对用户说话的声音进行采集得到的数据。与业务相关联的第一关键词例如可以为业务的名称、业务的名称缩写、以及用于表示业务的编号等等。计算机设备通过对第一语音数据进行语音识别,得到第一语音数据中与业务相关联的第一关键词,例如业务的名称,则根据业务的名称确定第一业务属性信息。例如第一语音数据为“我要办理银行卡”,第一关键词为“银行卡”,可以通过获取第一关键词的前后词语确定第一业务属性信息,例如确定出第一业务属性信息包括“办理银行卡”。
具体实现中,计算机设备可以采用ASR技术或者其他语音识别技术对语音数据进行识别,得到第一语音数据中与业务相关联的第一关键词,根据第一关键词确定第一业务属性信息。
s22,对第一语音数据进行转换,得到第一语音数据对应的第一文本数据。
这里,由于第一语音数据为语音类型的数据,可以将语音类型的数据转换为文本类型的数据,得到第一文本数据。
s23,对第一文本数据进行关键词提取,得到第一文本数据中与业务相关联的第二关键词,根据第二关键词确定第一业务属性信息。
这里,第一关键词和第二关键词可以相同,第一关键词与第二关键词也可以不同。计算机设备将第一语音数据转换为第一文本数据后,对第一文本数据进行关键词提取,得到第一文本数据中与业务相关联的第二关键词,根据第二关键词确定第一业务属性信息。
具体实现中,计算机设备首先对第一文本数据进行分词处理,将第一文本数据划分为至少一个分词;获取停用词集合,停用词集合中包括至少一个与业务无关的词语;在停用词集合中查找与该至少一个分词相匹配的目标词语;删除该至少一个分词中的目标词语;对删除该目标词语后的至少一个分词进行关键词提取,得到第二关键词,根据第二关键词确定第一业务属性信息。
例如,第一文本数据为“我想办理银行卡”,分词处理的结果即为“我想办理银行卡”,从而分成了4个分词,然后将这4个分词分别与停用词集合中的各个停用词进行匹配,若匹配到“我”、“想”这2个分词,则删除这2个分词,从而得到“办理银行卡”,对“办理银行卡”进行关键词提取,得到第二关键词“银行卡”,则根据第二关键词确定第一业务属性信息。
具体实现中,可以根据具体需求选择对第一语音数据进行语音识别,或者将第一语音数据转换为文本数据进行关键词提取,例如,语音识别的成本较低,则在节省成本的情况下,采用语音识别;或者,语音数据转换为文本数据进行关键词提取的准确度较高,则在提高识别准确度的情况下,采用语音数据转换为文本数据进行关键词提取。
通过对第一语音数据进行语音识别,或者将第一语音数据转换为文本数据进行转换,并对文本数据进行关键词提取,可以得到第一业务属性信息,从而可以根据第一业务属性信息确定生物识别引擎以及第一业务平台,进而可以进行相应的业务处理。
在一个实施例中,该第一业务属性信息包括第一业务的业务等级,上述步骤S103中可包括如下步骤s31~s32。
s31,获取共享识别引擎集合中的识别引擎的识别等级,识别引擎的识别等级用于反映识别引擎识别多媒体数据的准确度。
s32,将共享识别引擎集合中识别等级与第一业务的业务等级匹配的识别引擎,确定为目标识别引擎。
在步骤s31~s32中,识别引擎的识别等级越高,识别引擎识别多媒体数据的准确度越高;识别引擎的识别等级越低,识别引擎识别多媒体数据的准确度越低。第一业务的业务等级越高,则表示处理第一业务需要获取的识别数据的识别等级越高;第一业务的业务等级越低,则表示处理第一业务需要获取的识别数据的识别等级越低。例如,处理第一业务需要获取的识别数据为语音数据,表示第一业务的业务等级较低,则与第一业务的业务等级匹配度识别引擎的识别等级较低;处理第一业务需要获取的识别数据为面部数据,表示第一业务的业务等级较高,则与第一业务的业务等级匹配度识别引擎的识别等级较高。
可选的,在处理第一业务需要获取的识别数据为语音数据、指纹数据以及面部数据中的至少两种的情况下,可以根据识别数据的类型确定第一业务的业务等级。例如,处理第一业务需要获取的识别数据包括语音数据、指纹数据以及面部数据时,则第一业务的业务等级较高;处理第一业务需要获取的识别数据包括语音数据和指纹数据时,则第一业务的业务等级较低。例如,处理第一业务1-第一业务4需要获取的识别数据分别包括识别数据1-识别数据4,且识别数据1包括语音数据和指纹数据、识别数据2包括语音数据和面部数据、识别数据3包括指纹数据和面部数据、识别数据4包括语音数据、指纹数据以及面部数据,则第一业务1的业务等级小于第一业务2的业务等级,第一业务2的业务等级小于第一业务3的业务等级,第一业务3的业务等级小于第一业务4的业务等级。
通过获取共享识别引擎集合中的识别引擎的识别等级,将共享识别引擎集合中识别等级与第一业务的业务等级匹配的识别引擎,确定为目标识别引擎。在第一业务的业务等级较低的情况下,可以采用识别等级较低的识别引擎,从而可以节省成本;在第一业务的业务等级较高的情况下,可以采用识别等级较高的识别引擎,从而可以提高识别多媒体数据的准确度。
在一个实施例中,该第一业务属性信息包括第一业务的业务收益,上述步骤S103中可包括如下步骤s41~s42。
s41,获取共享识别引擎集合中的识别引擎的识别成本。
s42,将共享识别引擎集合中识别成本与第一业务的业务收益匹配的识别引擎,确定为目标识别引擎。
在步骤s41~s42中,第一业务的业务收益可以为对第一业务的预期收益,识别引擎的识别成本是指购买或者使用该识别引擎所需支出的货币的数量,识别引擎的识别成本越低,则第一业务的业务收益越高;识别引擎的识别成本越高,则第一业务的业务收益越低。计算机设备通过获取共享识别引擎集合中的识别引擎的识别成本,将共享识别引擎集合中识别成本与第一业务的业务收益匹配的识别引擎,确定为目标识别引擎。在第一业务的业务收益较高的情况下,采用识别成本较低的识别引擎识别多媒体数据,可以实现降低识别成本,从而提高第一业务的业务收益。
可选的,请参见图2,图2是本申请实施例提供的一种数据处理方法的流程示意图。该方法应用于计算机设备;如图2所示,该方法包括以下步骤。
S201,从终端中获取关于第一业务的第一多媒体数据。
S202,对第一多媒体数据进行识别,得到第一业务属性信息。
S203,从共享识别引擎集合中确定与第一业务属性信息匹配的识别引擎,作为目标识别引擎。
S204,输出关于处理第一业务的提示信息。
S205,从终端中获取针对提示信息所发送的第二多媒体数据。
这里,第二多媒体数据包括第一视频数据和第二语音数据,步骤S201~S205的具体实现方式可参考图1对应的实施例中步骤S101~S105的描述,此处不再赘述。
S206,根据第一视频数据获取终端对应的用户的第一图像。
这里,第一视频数据为终端采集的用户根据处理第一业务的提示信息进行回复得到的视频数据。第一视频数据中包括用户的面部图像。
计算机设备可以每隔预设时间对第一视频数据进行截取,得到包含用户面部的第一图像,得到终端对应的用户的第一图像。例如,可以每隔0.5秒钟截取第一视频数据中的图像,得到第一图像。例如第一视频数据的时长为2秒,则获取到用户的第一图像的数量为4张。
S207,将第一图像、第一视频数据以及第二语音数据发送至第一业务平台,以使第一业务平台根据第一图像验证终端的合法性,在终端具有合法性时,采用目标识别引擎对第一视频数据以及第二语音数据进行识别,处理第一业务。
这里,第二语音数据为终端采集的用户根据处理第一业务的提示信息进行回复的语音数据。计算机设备将第一图像、第一视频数据以及第二语音数据发送至第一业务平台以使第一业务平台根据第一图像验证终端的合法性,在终端具有合法性时,采用目标识别引擎对第一视频数据以及第二语音数据进行识别,处理第一业务。
具体实现中,第一业务平台获取到第一图像、第一视频数据以及第二语音数据后,可以采用目标识别引擎对第一图像进行识别,确定第一图像中用户的面部图像与第一业务平台存储的用户图像是否为同一用户的面部图像,若是,则确定终端具有合法性,则采用目标识别引擎对第一视频数据以及第二语音数据进行识别,处理第一业务。若否,则确定终端不具有合法性,则生成用于指示终端不具有合法性的警示信息,以使用户根据该警示信息进行姿态调整。
在一种可能的实现方式中,第一业务平台采用目标识别引擎对第一视频数据以及第二语音数据进行识别时,可以获取第一视频数据中与第二语音数据对应的用户的第三图像,即从第一视频数据中获取用户根据第一业务的提示信息回答问题时的第三图像,该第三图像中包含用户的面部图像。通过对第三图像进行微表情识别,从而根据用户回答问题时的微表情确定用户回答的问题的真实性。若通过微表情识别确定用户回答的问题真实性较高,则处理第一业务。若通过微表情识别确定用户回答的问题真实性较低,则发送用于二次验证用户身份的指示信息或再次输出用户微表情异常的问题。若二次验证通过或用户再次回答该问题时的表情指示用户回答的问题真实性较高,则处理第一业务。若二次验证未通过或用户再次回答该问题时的表情指示用户回答的问题真实性较低,则输出用于指示用户在第一业务平台对应的人工业务办理处进行业务办理,并结束处理第一业务。
通过获取第一视频数据中的第一图像,并发送第一图像至第一业务平台进行验证,可以提高用户身份的真实性,以及第一业务平台通过对第一视频数据中的第三图像进行微表情识别,可以识别用户回答的问题的真实性,从而实现二次验证用户的身份信息,提高业务办理的准确性。
在一个实施例中,上述步骤方法可包括如下步骤s51~s54。
s51,若获取到第一业务平台发送的用于指示终端不具有合法性的警示信息,则输出用于指示用户进行姿态调整的调整信息。
s52,获取终端针对调整信息发送的第三多媒体数据,第三多媒体数据包括第三视频数据。
s53,根据第三视频数据获取用户的第二图像。
s54,将第二图像发送至第一业务平台,以使第一业务平台根据第二图像验证终端的合法性。
在步骤s51~s54中,若计算机设备获取到第一业务平台发送的用于指示终端不具有合法性的警示信息,则输出用于指示用户进行姿态调整的调整信息,以使用户根据该调整信息进行姿态调整,例如,用户的面部未对准终端的摄像头时,则调整后的用户面部对准终端的摄像头;或者,终端的摄像头中包括用户A和用户B的情况下,且用户A为需要办理第一业务的用户,则调整后的终端的摄像头中只包括用户A。
计算机设备获取终端针对调整信息发送的第三多媒体数据,第三多媒体数据包括第三视频数据;根据第三视频数据获取用户的第二图像;将第二图像发送至第一业务平台,以使第一业务平台根据第二图像验证终端的合法性。第二图像中包括用户的面部图像,若第二图像与第一业务平台中存储的用户面部图像为同一用户的面部图像,则终端具有合法性,处理第一业务。若第二图像与第一业务平台中存储的用户面部图像不为同一用户的面部图像,则终端不具有合法性,则结束处理第一业务,输出用于指示用户在第一业务平台对应的人工业务办理处进行业务办理,并结束处理第一业务。通过在验证第一终端不具有合法性的情况下,通过输出调整信息提示用户进行姿态调整,可以实现对终端合法性的验证,从而提高用户身份信息验证的真实性。
上面介绍了本申请实施例的方法,下面介绍本申请实施例的装置。
参见图3,图3是本申请实施例提供的一种数据处理装置的组成结构示意图,上述数据处理装置可以是运行于计算机设备中的一个计算机程序(包括程序代码),例如该数据处理装置为一个应用软件;该装置可以用于执行本申请实施例提供的方法中的相应步骤。该装置30包括:第一获取模块301,用于从终端中获取关于第一业务的第一多媒体数据;数据识别模块302,用于对该第一多媒体数据进行识别,得到该第一业务属性信息,该第一业务属性信息包括该第一业务的业务等级或者该第一业务的业务收益中的至少一种;引擎确定模块303,用于从共享识别引擎集合中确定与该第一业务属性信息匹配的识别引擎,作为目标识别引擎;信息输出模块304,用于输出关于处理该第一业务的提示信息;第二获取模块305,用于从该终端中获取针对该提示信息所发送的第二多媒体数据;业务处理模块306,用于将该第二多媒体数据发送至第一业务平台,以使该第一业务平台采用该目标识别引擎对该第二多媒体数据进行识别,处理该第一业务。
可选的,该信息输出模块304,用于:根据该第一业务的标识确定处理该第一业务平台;从该第一业务平台中获取关于处理该第一业务的提示信息;输出该第一提示信息。
可选的,该第一多媒体数据包括第一语音数据,该数据识别模块302,具体用于:对该第一语音数据进行语音识别,得到该第一语音数据中与业务相关联的第一关键词,根据该第一关键词确定该第一业务属性信息;或者,对该第一语音数据进行转换,得到该第一语音数据对应的第一文本数据;对该第一文本数据进行关键词提取,得到该第一文本数据中与业务相关联的第二关键词;根据该第二关键词确定该第一业务属性信息。
可选的,该第一业务属性信息包括该第一业务的业务等级;该引擎确定模块303,具体用于:获取该共享识别引擎集合中的识别引擎的识别等级,该识别引擎的识别等级用于反映该识别引擎识别多媒体数据的准确度;将该共享识别引擎集合中识别等级与该第一业务的业务等级匹配的识别引擎,确定为该目标识别引擎。
可选的,该第一业务属性信息包括该第一业务的业务收益;该引擎确定模块303,具体用于:获取该共享识别引擎集合中的识别引擎的识别成本;将该共享识别引擎集合中识别成本与该第一业务的业务收益匹配的识别引擎,确定为该目标识别引擎。
可选的,该第二多媒体数据包括第一视频数据和第二语音数据;该业务处理模块306,具体用于:根据该第一视频数据获取该终端对应的用户的第一图像;将该第一图像、该第一视频数据以及该第二语音数据发送至该第一业务平台,以使该第一业务平台根据该第一图像验证该终端的合法性,在该终端具有合法性时,采用该目标识别引擎对该第一视频数据以及第二语音数据进行识别,处理该第一业务。
可选的,该装置还包括:调整模块307,用于:若获取到该第一业务平台发送的用于指示该终端不具有合法性的警示信息,则输出用于指示该用户进行姿态调整的调整信息;获取该终端针对该调整信息发送的第三多媒体数据,该第三多媒体数据包括第三视频数据;根据该第三视频数据获取该用户的第二图像;将该第二图像发送至该第一业务平台,以使该第一业务平台根据该第二图像验证该终端的合法性。
需要说明的是,图3对应的实施例中未提及的内容可参见方法实施例的描述,这里不再赘述。
本申请实施例中,通过对第一多媒体数据进行识别,可以获取到与第一业务对应的第一业务属性信息。通过确定第一业务对应的目标识别引擎,在后续处理第一业务时,使用该目标识别引擎进行识别,处理第一业务。由于共享识别引擎集合中包括多个识别引擎,即该种方式可以将多个识别引擎集中在共享识别引擎集合中,不同业务均可共享该集合中的识别引擎,不需要为不同业务定制识别引擎,可以避免资源浪费,节省硬件资源的投入,从而节省成本。进一步的,输出关于处理第一业务的提示信息,从终端中获取针对提示信息所发送的第二多媒体数据,通过输出提示信息,终端可以采集用户根据该提示信息进行回复得到的第二多媒体数据。将第二多媒体数据发送至第一业务平台,以使第一业务平台采用目标识别引擎对第二多媒体数据进行识别,处理第一业务。在用户需要进行业务办理时,只需要通过获取多媒体数据中的业务属性信息,确定对应的业务以及业务对应的识别引擎,将第一业务对应的多媒体数据发送至第一业务平台,第一业务平台即可采用对应的识别引擎进行识别,处理第一业务。可以实现将确定识别引擎和处理业务两种流程进行分离,实现快速对接到业务处理平台进行业务处理。
参见图4,图4是本申请实施例提供的一种计算机设备的组成结构示意图。如图4所示,上述计算机设备40可以包括:处理器401,网络接口404和存储器405,此外,上述计算机设备40还可以包括:用户接口403,和至少一个通信总线402。其中,通信总线402用于实现这些组件之间的连接通信。其中,用户接口403可以包括显示屏(Display)、键盘(Keyboard),可选用户接口403还可以包括标准的有线接口、无线接口。网络接口404可选的可以包括标准的有线接口、无线接口(如WI-FI接口)。存储器405可以是高速RAM存储器,也可以是非易失性的存储器(non-volatile memory),例如至少一个磁盘存储器。存储器405可选的还可以是至少一个位于远离前述处理器401的存储装置。如图4所示,作为一种计算机可读存储介质的存储器405中可以包括操作***、网络通信模块、用户接口模块以及设备控制应用程序。
在图4所示的计算机设备40中,网络接口404可提供网络通讯功能;而用户接口403主要用于为用户提供输入的接口;而处理器401可以用于调用存储器405中存储的设备控制应用程序,以实现:从终端中获取关于第一业务的第一多媒体数据;对该第一多媒体数据进行识别,得到该第一业务属性信息,该第一业务属性信息包括该第一业务的业务等级或者该第一业务的业务收益中的至少一种;从共享识别引擎集合中确定与该第一业务属性信息匹配的识别引擎,作为目标识别引擎;输出关于处理该第一业务的提示信息;从该终端中获取针对该提示信息所发送的第二多媒体数据;将该第二多媒体数据发送至第一业务平台,以使该第一业务平台采用该目标识别引擎对该第二多媒体数据进行识别,处理该第一业务。
应当理解,本申请实施例中所描述的计算机设备40可执行前文图1、图2所对应实施例中对上述数据处理方法的描述,也可执行前文图3所对应实施例中对上述数据处理装置的描述,在此不再赘述。另外,对采用相同方法的有益效果描述,也不再进行赘述。
本申请实施例中,通过对第一多媒体数据进行识别,可以获取到与第一业务对应的第一业务属性信息。通过确定第一业务对应的目标识别引擎,在后续处理第一业务时,使用该目标识别引擎进行识别,处理第一业务。由于共享识别引擎集合中包括多个识别引擎,即该种方式可以将多个识别引擎集中在共享识别引擎集合中,不同业务均可共享该集合中的识别引擎,不需要为不同业务定制识别引擎,可以避免资源浪费,节省硬件资源的投入,从而节省成本。进一步的,输出关于处理第一业务的提示信息,从终端中获取针对提示信息所发送的第二多媒体数据,通过输出提示信息,终端可以采集用户根据该提示信息进行回复得到的第二多媒体数据。将第二多媒体数据发送至第一业务平台,以使第一业务平台采用目标识别引擎对第二多媒体数据进行识别,处理第一业务。在用户需要进行业务办理时,只需要通过获取多媒体数据中的业务属性信息,确定对应的业务以及业务对应的识别引擎,将第一业务对应的多媒体数据发送至第一业务平台,第一业务平台即可采用对应的识别引擎进行识别,处理第一业务。可以实现将确定识别引擎和处理业务两种流程进行分离,实现快速对接到业务处理平台进行业务处理。
本申请实施例还提供一种计算机可读存储介质,该计算机可读存储介质存储有计算机程序,该计算机程序包括程序指令,该程序指令当被计算机执行时使该计算机执行如前述实施例该的方法,该计算机可以为上述提到的计算机设备的一部分。例如为上述的处理器401。
可选的,本申请涉及的存储介质如计算机可读存储介质可以是非易失性的,也可以是易失性的。
作为示例,程序指令可被部署在一个计算机设备上执行,或者被部署位于一个地点的多个计算机设备上执行,又或者,在分布在多个地点且通过通信网络互连的多个计算机设备上执行,分布在多个地点且通过通信网络互连的多个计算机设备可以组成区块链网络。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程,是可以通过计算机程序来指令相关的硬件来完成,该的程序可存储于计算机可读取存储介质中,该程序在执行时,可包括如上述各方法的实施例的流程。其中,该的存储介质可为磁碟、光盘、只读存储记忆体(Read-Only Memory,ROM)或随机存储记忆体(Random Access
Memory,RAM)等。
以上所揭露的仅为本申请较佳实施例而已,当然不能以此来限定本申请之权利范围,因此依本申请权利要求所作的等同变化,仍属本申请所涵盖的范围。
Claims (20)
- 一种数据处理方法,其中,包括:从终端中获取关于第一业务的第一多媒体数据;对所述第一多媒体数据进行识别,得到所述第一业务属性信息,所述第一业务属性信息包括所述第一业务的业务等级或者所述第一业务的业务收益中的至少一种;从共享识别引擎集合中确定与所述第一业务属性信息匹配的识别引擎,作为目标识别引擎;输出关于处理所述第一业务的提示信息;从所述终端中获取针对所述提示信息所发送的第二多媒体数据;将所述第二多媒体数据发送至第一业务平台,以使所述第一业务平台采用所述目标识别引擎对所述第二多媒体数据进行识别,处理所述第一业务。
- 根据权利要求1所述的方法,其中,所述第一业务属性信息还包括所述第一业务的标识,所述输出关于处理所述第一业务的提示信息,包括:根据所述第一业务的标识确定处理所述第一业务平台;从所述第一业务平台中获取关于处理所述第一业务的提示信息;输出所述第一提示信息。
- 根据权利要求1所述的方法,其中,所述第一多媒体数据包括第一语音数据,所述对所述第一多媒体数据进行识别,得到所述第一业务属性信息,包括:对所述第一语音数据进行语音识别,得到所述第一语音数据中与业务相关联的第一关键词,根据所述第一关键词确定所述第一业务属性信息;或者,对所述第一语音数据进行转换,得到所述第一语音数据对应的第一文本数据;对所述第一文本数据进行关键词提取,得到所述第一文本数据中与业务相关联的第二关键词;根据所述第二关键词确定所述第一业务属性信息。
- 根据权利要求1所述的方法,其中,所述第一业务属性信息包括所述第一业务的业务等级;所述从共享识别引擎集合中确定与所述第一业务属性信息匹配的识别引擎,作为目标识别引擎,包括:获取所述共享识别引擎集合中的识别引擎的识别等级,所述识别引擎的识别等级用于反映所述识别引擎识别多媒体数据的准确度;将所述共享识别引擎集合中识别等级与所述第一业务的业务等级匹配的识别引擎,确定为所述目标识别引擎。
- 根据权利要求1所述的方法,其中,所述第一业务属性信息包括所述第一业务的业务收益,所述从共享识别引擎集合中确定与所述第一业务属性信息匹配的识别引擎,作为目标识别引擎,包括:获取所述共享识别引擎集合中的识别引擎的识别成本;将所述共享识别引擎集合中识别成本与所述第一业务的业务收益匹配的识别引擎,确定为所述目标识别引擎。
- 根据权利要求1所述的方法,其中,所述第二多媒体数据包括第一视频数据和第二语音数据;所述将所述第二多媒体数据发送至第一业务平台,以使所述第一业务平台采用所述目标识别引擎对所述第二多媒体数据进行识别,处理所述第一业务,包括:根据所述第一视频数据获取所述终端对应的用户的第一图像;将所述第一图像、所述第一视频数据以及所述第二语音数据发送至所述第一业务平台,以使所述第一业务平台根据所述第一图像验证所述终端的合法性,在所述终端具有合法性时,采用所述目标识别引擎对所述第一视频数据以及第二语音数据进行识别,处理所述第一业务。
- 根据权利要求6所述的方法,其中,所述方法还包括:若获取到所述第一业务平台发送的用于指示所述终端不具有合法性的警示信息,则输出用于指示所述用户进行姿态调整的调整信息;获取所述终端针对所述调整信息发送的第三多媒体数据,所述第三多媒体数据包括第三视频数据;根据所述第三视频数据获取所述用户的第二图像;将所述第二图像发送至所述第一业务平台,以使所述第一业务平台根据所述第二图像验证所述终端的合法性。
- 一种数据处理装置,其中,包括:第一获取模块,用于从终端中获取关于第一业务的第一多媒体数据;数据识别模块,用于对所述第一多媒体数据进行识别,得到所述第一业务属性信息,所述第一业务属性信息包括所述第一业务的业务等级或者所述第一业务的业务收益中的至少一种;引擎确定模块,用于从共享识别引擎集合中确定与所述第一业务属性信息匹配的识别引擎,作为目标识别引擎;信息输出模块,用于输出关于处理所述第一业务的提示信息;第二获取模块,用于从所述终端中获取针对所述提示信息所发送的第二多媒体数据;业务处理模块,用于将所述第二多媒体数据发送至第一业务平台,以使所述第一业务平台采用所述目标识别引擎对所述第二多媒体数据进行识别,处理所述第一业务。
- 一种计算机设备,其中,包括:处理器、存储器以及网络接口;所述处理器与所述存储器、所述网络接口相连,其中,所述网络接口用于提供数据通信功能,所述存储器用于存储程序代码,所述处理器用于调用所述程序代码,以执行以下方法:从终端中获取关于第一业务的第一多媒体数据;对所述第一多媒体数据进行识别,得到所述第一业务属性信息,所述第一业务属性信息包括所述第一业务的业务等级或者所述第一业务的业务收益中的至少一种;从共享识别引擎集合中确定与所述第一业务属性信息匹配的识别引擎,作为目标识别引擎;输出关于处理所述第一业务的提示信息;从所述终端中获取针对所述提示信息所发送的第二多媒体数据;将所述第二多媒体数据发送至第一业务平台,以使所述第一业务平台采用所述目标识别引擎对所述第二多媒体数据进行识别,处理所述第一业务。
- 根据权利要求9所述的计算机设备,其中,所述第一业务属性信息还包括所述第一业务的标识,所述输出关于处理所述第一业务的提示信息时,具体执行:根据所述第一业务的标识确定处理所述第一业务平台;从所述第一业务平台中获取关于处理所述第一业务的提示信息;输出所述第一提示信息。
- 根据权利要求9所述的计算机设备,其中,所述第一多媒体数据包括第一语音数据,所述对所述第一多媒体数据进行识别,得到所述第一业务属性信息时,具体执行:对所述第一语音数据进行语音识别,得到所述第一语音数据中与业务相关联的第一关键词,根据所述第一关键词确定所述第一业务属性信息;或者,对所述第一语音数据进行转换,得到所述第一语音数据对应的第一文本数据;对所述第一文本数据进行关键词提取,得到所述第一文本数据中与业务相关联的第二关键词;根据所述第二关键词确定所述第一业务属性信息。
- 根据权利要求9所述的计算机设备,其中,所述第一业务属性信息包括所述第一业务的业务等级;所述从共享识别引擎集合中确定与所述第一业务属性信息匹配的识别引擎,作为目标识别引擎时,具体执行:获取所述共享识别引擎集合中的识别引擎的识别等级,所述识别引擎的识别等级用于反映所述识别引擎识别多媒体数据的准确度;将所述共享识别引擎集合中识别等级与所述第一业务的业务等级匹配的识别引擎,确定为所述目标识别引擎;或者,所述第一业务属性信息包括所述第一业务的业务收益,所述从共享识别引擎集合中确定与所述第一业务属性信息匹配的识别引擎,作为目标识别引擎时,具体执行:获取所述共享识别引擎集合中的识别引擎的识别成本;将所述共享识别引擎集合中识别成本与所述第一业务的业务收益匹配的识别引擎,确定为所述目标识别引擎。
- 根据权利要求9所述的计算机设备,其中,所述第二多媒体数据包括第一视频数据和第二语音数据;所述将所述第二多媒体数据发送至第一业务平台,以使所述第一业务平台采用所述目标识别引擎对所述第二多媒体数据进行识别,处理所述第一业务时,具体执行:根据所述第一视频数据获取所述终端对应的用户的第一图像;将所述第一图像、所述第一视频数据以及所述第二语音数据发送至所述第一业务平台,以使所述第一业务平台根据所述第一图像验证所述终端的合法性,在所述终端具有合法性时,采用所述目标识别引擎对所述第一视频数据以及第二语音数据进行识别,处理所述第一业务。
- 根据权利要求13所述的计算机设备,其中,所述处理器还用于执行:若获取到所述第一业务平台发送的用于指示所述终端不具有合法性的警示信息,则输出用于指示所述用户进行姿态调整的调整信息;获取所述终端针对所述调整信息发送的第三多媒体数据,所述第三多媒体数据包括第三视频数据;根据所述第三视频数据获取所述用户的第二图像;将所述第二图像发送至所述第一业务平台,以使所述第一业务平台根据所述第二图像验证所述终端的合法性。
- 一种计算机可读存储介质,其中,所述计算机可读存储介质存储有计算机程序,所述计算机程序包括程序指令,所述程序指令当被处理器执行时使所述处理器执行以下方法:从终端中获取关于第一业务的第一多媒体数据;对所述第一多媒体数据进行识别,得到所述第一业务属性信息,所述第一业务属性信息包括所述第一业务的业务等级或者所述第一业务的业务收益中的至少一种;从共享识别引擎集合中确定与所述第一业务属性信息匹配的识别引擎,作为目标识别引擎;输出关于处理所述第一业务的提示信息;从所述终端中获取针对所述提示信息所发送的第二多媒体数据;将所述第二多媒体数据发送至第一业务平台,以使所述第一业务平台采用所述目标识别引擎对所述第二多媒体数据进行识别,处理所述第一业务。
- 根据权利要求15所述的计算机可读存储介质,其中,所述第一业务属性信息还包括所述第一业务的标识,所述输出关于处理所述第一业务的提示信息时,具体执行:根据所述第一业务的标识确定处理所述第一业务平台;从所述第一业务平台中获取关于处理所述第一业务的提示信息;输出所述第一提示信息。
- 根据权利要求15所述的计算机可读存储介质,其中,所述第一多媒体数据包括第一语音数据,所述对所述第一多媒体数据进行识别,得到所述第一业务属性信息时,具体执行:对所述第一语音数据进行语音识别,得到所述第一语音数据中与业务相关联的第一关键词,根据所述第一关键词确定所述第一业务属性信息;或者,对所述第一语音数据进行转换,得到所述第一语音数据对应的第一文本数据;对所述第一文本数据进行关键词提取,得到所述第一文本数据中与业务相关联的第二关键词;根据所述第二关键词确定所述第一业务属性信息。
- 根据权利要求15所述的计算机可读存储介质,其中,所述第一业务属性信息包括所述第一业务的业务等级;所述从共享识别引擎集合中确定与所述第一业务属性信息匹配的识别引擎,作为目标识别引擎时,具体执行:获取所述共享识别引擎集合中的识别引擎的识别等级,所述识别引擎的识别等级用于反映所述识别引擎识别多媒体数据的准确度;将所述共享识别引擎集合中识别等级与所述第一业务的业务等级匹配的识别引擎,确定为所述目标识别引擎;或者,所述第一业务属性信息包括所述第一业务的业务收益,所述从共享识别引擎集合中确定与所述第一业务属性信息匹配的识别引擎,作为目标识别引擎时,具体执行:获取所述共享识别引擎集合中的识别引擎的识别成本;将所述共享识别引擎集合中识别成本与所述第一业务的业务收益匹配的识别引擎,确定为所述目标识别引擎。
- 根据权利要求15所述的计算机可读存储介质,其中,所述第二多媒体数据包括第一视频数据和第二语音数据;所述将所述第二多媒体数据发送至第一业务平台,以使所述第一业务平台采用所述目标识别引擎对所述第二多媒体数据进行识别,处理所述第一业务时,具体执行:根据所述第一视频数据获取所述终端对应的用户的第一图像;将所述第一图像、所述第一视频数据以及所述第二语音数据发送至所述第一业务平台,以使所述第一业务平台根据所述第一图像验证所述终端的合法性,在所述终端具有合法性时,采用所述目标识别引擎对所述第一视频数据以及第二语音数据进行识别,处理所述第一业务。
- 根据权利要求19所述的计算机可读存储介质,其中,所述程序指令当被处理器执行时还用于使所述处理器执行:若获取到所述第一业务平台发送的用于指示所述终端不具有合法性的警示信息,则输出用于指示所述用户进行姿态调整的调整信息;获取所述终端针对所述调整信息发送的第三多媒体数据,所述第三多媒体数据包括第三视频数据;根据所述第三视频数据获取所述用户的第二图像;将所述第二图像发送至所述第一业务平台,以使所述第一业务平台根据所述第二图像验证所述终端的合法性。
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